dataesr/scientific-paragraphs-categorization huggingface dataset:
Calcium imaging data summary The data obtained from the different imaging experiments consisted of the following. A1 recordings of auditory responses: 4616 cortical neurons from 29 L2/3 imaging areas in 11 animals. In 18 sessions (seven mice), animals were also stimulated with looming visual stimuli and bimodal stimuli from which we observed no response or modulation. 7757 neurons from upper layer 5 recorded in 12 sessions across four mice during sound only protocols. 2474 neurons projecting to V1 recorded in 15 sessions across three mice during sound only protocols repeated in light or in dark conditions. V1 recordings: 18,925 neurons from 35 L2/3 imaging areas in nine mice, and included the three 'dark', 'dim light' and 'bimodal' blocks in random order. Eye tracking was performed in 23 of these sessions (9849 neurons, seven mice; only these sessions where used for the analyses of the 'dim light' and 'bimodal' context, which required filtering out trials with saccades).
Calcium imaging pre-processing Data analysis was performed with custom-made Matlab scripts available upon request. Every frame recorded was corrected for horizontal motion to a template image using rigid body registration (all sessions with visible z motion were discarded). Regions of interest were then automatically selected and human checked as the cell bodies of neurons with visually identifiable activity  (Roland et al. , 2017)  and the mean fluorescence signal F(t) was extracted for each region. We also estimated the local neuropil signal F np (t) for each neuron and subtracted a fraction of it from the neuron signal F c (t) =F(t) -0. 7 F np (t). This fraction was set to 0. 7, according to a previous calibration for GCAMP6s in mouse visual cortex  (Chen et al. , 2013b). Baseline fluorescence F c0 was calculated as the minimum of a Gaussian-filtered trace over the 5 neighboring 5 s imaging blocks and fluorescence variations were computed as f(t) = (F c (t) -F c0 )/F c0.
Calcium imaging analysis Data analysis was performed with custom-made Matlab and Python scripts. Clustering of auditory responses in the auditory and visual cortices (Figure  1 ), and of bimodal conditions in the visual cortex (Figure  6 ) was performed using the following procedure. Deconvolved calcium responses were averaged across all valid trials (eye movement filtering). We then subtracted the baseline (average activity from 0 to 0. 5 s before stimulus onset) and the average activity profile in trials with no stimulation. Hierarchical clustering was performed using the Euclidean metric and Ward method for computing distance between clusters. To determine the number of clusters, we moved down the clustering threshold (100 clusters for all AC datasets, 25 clusters for V1 data) until clusters became redundant (overclustering) as assessed visually.
Electrophysiology data analysis Single unit spikes were detected automatically using the klusa Suite (https://github. com/kwikteam/ phy). Spikes were then sorted using KlustaKwik spike sorting algorithm  (Harris et al. , 2000)  (Klusta, https://github. com/kwikteam/klusta). All posterior data analyses were performed using custom Python scripts. Firing rates were calculated in 25 ms time bins, averaging over 20 sound repetitions.
Acknowledgements We thank  A Fleischmann ,  Y Fre  ´gnac and  J Letzkus  for helpful discussions and comments on the manuscript. We thank  A Fleischmann  for providing CAV2-Cre viruses. This work was supported by the  Agence Nationale pour la Recherche (ANR  ' SENSEMAKER '), the  Marie Curie   FP7 Program  ( CIG 334581 ), the  Human Brain Project  ( SP3 -WP5. 2 ), the  European Research Council  ( ERC CoG Deepen) , the  Fyssen foundation , the  DIM 'Region Ile de France' , the  International Human Frontier Science Program Organization  ( CDA-0064-2015 ), the  Fondation pour l'Audition  ( Laboratory grant ),  E ´cole Doctorale Frontieres du Vivant (FdV) -Programme Bettencourt  and the  Paris-Saclay University (Lidex NeuroSaclay ,  IRS iCode and IRS Brainscopes).
Plasmodium data P. falciparum data are biallelic (i. e. , K t ¼ 2 for all t ¼ 1;. ; m) SNP data from monoclonal samples (Table  1 ). All data are published  (Echeverry et al. 2013; Nkhoma et al. 2013; Cerqueira et al. 2017; Omedo et al. 2017a,b; Taylor et al. 2017). They were obtained either from sparse genomewide panels of select markers, called barcodes, or from a dense whole-genome sequencing (WGS) data set; full details of sample collection and data generation can be found via the citations above, and references therein. Additional steps we took to process the data are as follows. Besides mapping SNP positions to the P. falciparum 3d7 v3 reference genome and recoding heteroallelic calls as missing [since all available samples were previously classified monoclonal  (Echeverry et al. 2013 )], we did not postprocess the Colombian data in any way.
Simulated data Unless otherwise stated, data were simulated under the HMM with genotyping error e ¼ 0:001 using positions sampled uniformly from the Thailand WGS data set and with frequencies as follows. Biallelic marker data were simulated using frequencies sampled from the Thailand WGS data set with probability proportional to minor allele frequency estimates (to compensate for the skew toward rare alleles in WGS data set). Polyallelic marker data were simulated using frequencies sampled from a Dirichlet distribution using parameter vector a with K t entries each equal to 100 to generate frequencies for approximately equifrequent alleles, and a with entries each equal to 1 to generate frequencies uniform over the K t 2 1 simplex, thus increasingly skewed toward rare alleles when K t. 2.
Data availability All data used in this study are either simulated or published previously. Additional steps we took to process the data are described in section Plasmodium data. The processed data and code necessary for confirming the conclusions of the article are available at https://github. com/ artaylor85/PlasmodiumRelatedness. All code was written in R (R Core Team 2018). Supplemental material available at FigShare: https://doi. org/10. 25386/genetics. 8977217.
; K t such that K9 t , K t (triangles). FIM, Fisher information matrix. comparable across data sets, e. g. , among SNP data sets, the leftmost centering of the Thailand 93-SNP distribution is not evidence that P. falciparum parasites from Thailand are less related than those from Kenya. Despite very different absolute values, each d IBS m distribution centers around h m max , the d IBS m expectation when r ¼ 0. Thus, we conclude that many parasite pairs in these real data sets are unrelated, as corroborated by estimates based on IBD  (Figure 5, bottom). The d IBS m distribution based on P. vivax data (Thailand MS) most closely approximates its partner rm distribution due to highly polymorphic MSs.
Relatedness of Plasmodium data For each data set, rm values range from 0 to 1, suggesting the presence of unrelated, partially related, and clonal parasites (Figure  5 , bottom plot). However, the vast majority are , 0:20. The skew toward lowly related parasite pairs is consistent with primary IBD-based analyses of the Thai P. falciparum data  (Cerqueira et al. 2017; Taylor et al. 2017) , as well as mean IBD fractions reported elsewhere  (Zhu et al. 2018). Though the majority are , 0:20, mean rm values vary. Variation is caused by several factors. First, the mean is sensitive to small but variable counts of highly related parasite pairs: proportions of rm. 0:5 range from 0. 003 in the Thailand 93-SNP data set (lowest mean rm ) to 0. 062 and 0. 065 in the Colombia and The Gambia data sets, respectively (highest mean rm ).
The HMM vs. the independence model The HMM was used to compute rm for biallelic P. falciparum data sets, all of which have m max. 24 (Table  1 ), whereas the independence model was used for the polyallelic P. vivax data set, Thai MS, whose m max ¼ 9. In this section, the performance of the HMM is compared to that of the independence model using data simulated under the HMM. The main difference between the HMM and the independence model is estimation uncertainty. Under a well-specified model, 95% C. I. s should have 95% coverage, i. e. , contain the value of r used to simulate the data 95% of the time. The HMM provides coverage close to 0. 95 for m. 24, while the independence model (misspecified) provides waning coverage for m. 24, especially when k is small. For m ¼ 24, both the HMM and the independence model provide similar coverage, above or around 0.
2019). Besides motif repeats within MSs and SNPs within microhaplotypes (presently overlooked), it is preferable to minimize dependence between markers. Dependence is a function of marker position and LD. When considering polyallelic markers, we sampled marker positions uniformly at random from the Thailand WGS data set. For microhaplotypes, a more realistic approach would draw from genomic intervals amenable to physical phasing and with high within-interval LD. If diverse windows are genomically clustered, this presents a trade-off between distance and cardinality. We do not consider the trade-off here, but it can be explored within the current framework and is a topic of future work. Regarding LD, some models commonly used in human genetics account for it  (Browning 2008; Browning and Browning 2010)  [also see  Brown et al.
Acknowledgments We thank all the authors of the Plasmodium data sets for either sharing their data, or making them freely available online for use here and elsewhere, and  Stephen Schaffner  for helpful discussions. P. E. J. gratefully acknowledges support from the  National Science Foundation  through grant  DMS-1712872  and the  Harvard Data Science Initiative. A. R. T. and C. O. B. are supported by a  Maximizing Investigators' Research Award for Early Stage Investigators  ( R35 GM-124715 ). This project was funded in part with Federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under grant number  U19 AI-110818  to the  Broad Institute  (D. E. N. ).
WGS, whole-genome sequencing. a Full details of sample collection and data generation can be found via the citations above, and references therein. Additional steps we took to process the data for use in this study are described in section Plasmodium data. b For each processed data set, n denotes the number of monoclonal P. falciparum samples. c For each processed data set, m max denotes the maximum number of successfully genotyped SNPs per sample. d For each processed data set, h mmax denotes the expected homozygosity (Equation3) averaged over m max. e For each processed data set, K9 mmax denotes the effective cardinality (Equation7) averaged over m max.
5), APs divide asymmetrically to self-renew and to give rise to a neuron that migrates toward the cortical plate (CP) (direct neurogenesis) or to an IP (IP-genic division) that migrates out of the ventricular zone (VZ) to form the subventricular zone (SVZ). IPs can either be IPNs that divide to give rise to 2 neurons or IPPs that divide to give rise to two IPNs as for the involvement of apoptosis in non pathological situations. In particular, in  [19] , the authors introduced a multitype Bellman-Harris branching process, which explicitly takes into account the cell cycle phases and generation number of amplifying neuroprogenitors. They took advantage of the stationary nature of adult neurogenesis on the short term to obtain experimentally plausible cell numbers (from raw data expressed as proportion of BrdU labeled cell types).
Acquisition and exploitation of experimental data To obtain data to fuel the model, we quantified three cell populations during cortical neurogenesis: APs, IPs, and Ns. For this quantification, we performed immunofluorescence on thin sections, with a combination of markers  [37] [38] [39]  (Table  2 , Additional file 3 and Fig. 3 ). The counting strategy is detailed in Methods. In order to estimate the proportion of IPPs and IPNs, we quantified the number of Pax6 + Tbr2 + progenitors, proposed previously to represent the pool of IPPs  [16, 40]. Figure  3f -h represents the experimental cell numbers in wild-type (blue) and Ftm -/-(red) cortices at different developmental stages. The three panels correspond respectively to the IPs (F), IPPs (G) and neurons (H). Experimental AP numbers were not used for the model calibration, since their proliferative divisions increase not only cell crowding in the pseudo-stratified VZ but also the surface of the VZ  [41].
Fitting results and parameter calibration on experimental data A priori information can be used for some of the model parameters, such as the durations of the cell cycle phases (gathered in Table  3 ) provided in  [6] , a study which provides a comprehensive description of the cell cycle in each progenitor type depending on the fate of its progeny. In order to distinguish IPPs and IPNs, the authors made use of the Tis21-GFP transgenic mouse line, in which GFP is selectively expressed in neuron-generating progenitors  [7]. Our experimental data consist in cell counts of intermediate progenitors and neurons, on a daily basis between E12 and E18. Double stained Pax6 + Tbr2 + cells are considered as a rough estimate of IPPs  [16, 43]. We thus have three series of datasets corresponding to the model outputs IPP(t), IP(t) and N(t), whose amplitude ranges are quite contrasted.
Indeed, the maximal number of neurons is clearly greater than that of intermediate progenitors, since neurons accumulate over time, while the dynamics of intermediate progenitors is transient. In the same way, the maximal number of IPP is smaller than that of IP. To fit the model parameters, it is thus natural to design a multi-objective function J(p) balancing rigorously the contribution of the different datasets, as detailed in the "Methods" section. We minimize J(p) over the parameter set P = p = (K AP , s + , t + , s -, t -, γ 0 , γ 1 , s γ , t γ ), p i ∈[ p i min , p i max ] , i = 1,. , 9. (18) The size of this set is reduced to nine parameters, as we have set β to 1, which amounts to neglecting direct neurogenesis. This choice was motivated by preliminary optimization trials, in which the estimated value of β(t) systematically happened to be non time-varying and close to 1.
We performed several simulations with different sets of parameters obtained from the minimization of J(p). The values of the criterion and parameter sets are gathered in Table  4. For each simulation, we display, in addition to the model outputs, • the coefficients C IP , C N and C IPP that indicate which of the three datasets entered the calibration. They are all equal to 1/3 if all three datasets are taken into  • the ratio between the number of produced neurons and the number of APs which have entered neurogenesis in the course of the simulation, from initial time t 0 = 0 to stopping time t = T, which we set equal to E20 in the numerical simulations r N AP (T) = N(T) T 0 F AP (t)dt , (19) which can also be directly estimated from the extension of formula  (17)  with time-dependent rates: r N AP (T) = 1 T T 0 1 + β(t) + 2β(t)γ (t)dt.
In contrast, these scenarios clearly differ as far as fitting IP numbers. Scenario 1 and 2 both fail to capture correctly the initial rise in IPs, which occurs too early with respect to the experimental data. Thanks to the introduction of the time variability in γ , scenario 2 does capture the decay in IPs, which improves the fit on IPs and neurons (it is reduced to the fourth of the value obtained with scenario 1). Scenario 3 is rather close to scenario 1 for Ns (even if the neuronal yield r N AP is slightly lowered) and total IPs. Yet, as expected, the fitting to IPP data is drastically improved and involves a downward translation of the γ (t) curve. There remains a shift to the right along the descending part of the IPP curve, which can be explained by the discrepancy observed between the experimental IP and IPP datasets (WT IPP values exceed IP values at E18.
5, see panels F and G of Fig. 3 ) at the end of neurogenesis. As discussed elsewhere, IPP are identified as Pax6 + Tbr2 + cells; this marker appears to be less reliable during late neurogenesis. The simulations displayed in Fig. 5  can be reproduced with an Ipython notebook, CEMONE (CEll based MOdel of NEurogenesis) which can be either downloaded from https://github. com/letsop/cemone or directly run on https://mybinder. org/v2/gh/letsop/cemone/master. From the results obtained on the control dataset, we directly performed the parameter optimization on the mutant dataset in a situation similar to scenario 3 (timedependent γ (t) and C IPP = 1/3, scenario 4 in Table  4 ). We compare the control and mutant results in Fig. 6. The differences observed in the experimental datasets mainly concern the IP (and IPP) peak, which is higher and narrower, and also occurs later in mutant mice compared to control.
In both the control and mutant cases, we had to optimize a fitting criterion with a relatively large number of parameters, entering the definition of F AP (t) and γ (t). Notwithstanding the relatively high dimension of the parameter space, the multi-criterion based optimization procedure following the lines of  [44]  has allowed us to take advantage of our multiple experimental datasets. In the case of the Ftm mutant, our simulations suggest that the perturbed dynamics of neurogenesis cannot be accounted for by a clear difference in IP proliferation mode or division rate. The model allows us to predict that obtaining a similar neuronal output with a shorter neurogenesis period requires an important and transient inflow of APs (F AP ). Given the shape of γ (t) on panel a of Fig. 6 , one could argue that a piece-wise constant function could have been chosen for γ (t).
Given a set of parameters estimated from data dealing only with IPs (possibly including IPPs) and neurons, we can derive (among several other model outputs), the neurogenic fraction. The computation of the neurogenic fraction with the parameters selected from our optimization procedure can thus be considered as a prediction of the model. Interestingly, this prediction is consistent with the results obtained in  [46]. In this work, the authors have taken advantage of the Tis21-GFP staining in combination with Pax6 or Tbr2, in addition to PH3, to identify the neurogenic fate of APs and IPs in a specific way. They have shown that a significant proportion of IP mitoses are not neurogenic and that very few AP mitoses are neurogenic, which corroborates the very low, or even neglectible value of direct neurogenesis from AP in our setup.
We follow the approach proposed in  [44]  to define a multi-objective optimization criterion J(p) J(p) = C IP J IP (p) -J IP 2 J max IP -J IP 2 + C IPP J IPP (p) -J IPP 2 J max IPP -J IPP 2 +C N J N (p) -J N 2 J max N -J N 2 (27) Algorithm 1: Numerical algorithm used to compute the cell densities and total cell numbers of intermediate progenitors IPPs and IPNs. Partial cell numbers in M and S phases are obtained similarly; the values on the internal boundaries delimitating each phase are interpolated using the two nearest nodes. The corresponding python code CEMONE can be downloaded from https://github. com/letsop/cemone for j = 1,. , N a do IPP 0 j = IPP 0 (j ) end for for j = 1,. , Ña -1 do IPN 0 j = IPN 0 (j ) end for IPN 0 Ña = IPN 0 (T N C ) for n = 1, 2,. do for j = 1,. , N a do IPP n j = IPP n-1 j-1 end for IPP n 0 = γ n β n F n AP IPN n 0 = (1 -γ n )β n F n AP + 2IPP n N a for j = 1,.
-Also, the rise (resp. decay) in F AP has to happen before (resp. after) the time when the maximal IP number is observed. • or a posteriori -Bounds on K AP are in order to remain inactive at the minimum. In  (27)  the three elementary criteria measure the fit with each the three datasets J IP (p) = N exp i=1 w IP i IP(t i , p) -IP exp i 2 , ( 28 ) J IPP (p) = N exp i=1 w IPP i IPP(t i , p) -IPP exp i 2 , ( 29 ) J N (p) = N exp i=1 w N i N(t i , p) -N exp i 2. ( 30 ) In each elementary criterion  (28) (29) (30)   27 ) uses the extremum values J IP and J max IP (respectively J IPP , J N and J max IPP , J max N ) obtained by minimization of J IP (p) (respectively J IPP (p), J N (p)) as explained in more details in Additional file 4. An interesting feature of this normalized multi objective function is that it enables us to compare the results of optimization procedures performed on different datasets (control and mutant), as it ensures that J(p) varies between 0 and 1.
The slope parameters s + , s -, and s γ have the lowest influence. We also perform an analysis of the sensitivity to noise in the data. We generate synthetic noisy datasets by adding normally distributed random noise N (0, σ ) to the deterministic outputs, and we compute the statistics of the parameters identified on single noisy datasets. As a whole, all parameters, except the slope parameters, are well estimated in the case of low noise (with an error less than 1% for K AP , t + , t -, and t γ , 1. 4% for γ 0 and 3. 6% for γ 1 ). As a result of this study, we conclude that the CMAES stochastic method, which overcomes the problem of sensitivity to the initial parameter guess, is more appropriate than a descent method.
Beyond our specific interest for Ftm/Rpgrip1l, one could easily use our framework to study other experimental situations in rodents. The quantitative effect of changing some parameter values can be tested and visualized in a user-friendly and interactive manner in the CEMONE environment (ipython notebook). This work was funded by the Agence Nationale pour la Recherche (ANR blanc "CILIAINTHEBRAIN", project 11-BSV2-0006 to S. S. M. ), the Fondation pour la Recherche Médicale (Equipe FRM DEQ20140329544 to S. S. M. ), the "Fondation ARC pour la recherche sur le cancer" (C, PJA 20171206591 grant to S. S. M. ), and Sorbonne Universités (Idex Super Emergence Program, project "NeuroMathMod" SU-15-R-EMR-05 to M. P. ). The funders had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
[6]ations of the IPP and IPN cell cycle phases at E14. 5 taken from Arai et al. [6]account in the calibration. If C IPP = 0, only the IP and N datasets enter the calibration with an equal weight C IP = C N = 1/2. • the elementary fit values J IP (p), J IPP (p) and J N (p) on either of the three datasets IP, IPP and N, which are combined to compute the global criterion J(p).
It is usually exported as a stack of 2D images in a given orientation (Fig. 6 ). The micro-CT dataset can then be processed with dedicated software for 3D visualisation (either through volume rendering or isosurface rendering, see Section 3. 5) or further analysis (see Tables  8  and 9  for comprehensive lists of software). The rendered images can be also used for the creation of 3D videos which are an excellent tool for sharing a preview of a micro-CT dataset  (Abel et al. 2012).
When the positions of the detector and the rotating platform are set, the calibration usually consists in acquiring a set of radiographs of a calibration object placed on the rotating platform. A greater magnification and resolution could decrease beam-hardening effects (see Chapter 3. 6), but will increase scanning duration. A big specimen size can prevent achieving high magnification and resolution while a smaller field of view is required for the identification of the smallest structures  (Dixon et al. 2018). For this reason, micro-CT datasets acquired at different resolutions can be combined in order to provide more information  (Dixon et al. 2018  and examples therein).