dataesr/scientific-paragraphs-categorization huggingface dataset:
This study was approved by a regional ethics committee (comitéconsultatif de protection des personnes dans la recherche biomedicale Tours-Reǵion Centre-Ouest 1 under registration number DC-2016-2651) and by the French regulatory agency (Agence de la Biomedecine, the national authority for organ procurement and transplantation in France, under registration number PFS16-005). Written informed consents were obtained for each patient and healthy donor according to the Declaration of Helsinki. All data were collected anonymously in a prospectively maintained database declared to the French Data Protection Authority. No potentially identifiable human images or data are presented in this study.
Flow cytometry analysis Briefly, cells were stained by viability dye, and then incubated with the appropriate antibody/reagent mix prior to fixation, as previously reported  (12, 45). A detailed list of antibodies and reagents used for staining is provided in Supplementary Table  2. Fluorescence minus one control or unstained cells served as negative staining control samples. Samples were acquired on a spectral cytometer AURORA using SpectroFlo ® software for unmixing (Cytek) and on a BD FACS Verse ™ cytometer using the BD FACsSuite software (BD Biosciences) for human and mouse samples, respectively. Data were analyzed using FlowJo v10 (BD Biosciences). Because of the very low relative proportion of iNKT cells among human PBMC (that can be less than 0. 01% of total CD3 (+) cells), a minimum of 50,000 CD3(+) cells were analyzed for all human samples.
Statistical analysis Statistical analysis was performed using Excel ® for Mac Os (Microsoft ® Corporation) and Graph Pad Prism software v7 (GraphPad, La Jolla, Inc. ). Quantitative data are presented as percentages and absolute numbers. Qualitative data are presented as mean (± standard error of the mean (SEM)). Non-parametric tests were used because of the small number of samples per group. Experimental and control groups were compared with the unpaired (independent) two-tailed Mann-Whitney U test. Spearman tests were performed for correlation analysis. Results were considered to be statistically significant when p<0. 05.
Monosynaptically restricted rabies viruses have been used for more than a decade for synaptic connectivity tracing. However, the verisimilitude of quantitative conclusions drawn from these experiments is largely unknown. The primary reason is the simple metrics commonly used, which generally disregard the effect of starter cell numbers. Here we present an experimental dataset with a broad range of starter cell numbers and explore their relationship with the number of input cells across the brain using descriptive statistics and modelling. We show that starter cell numbers strongly affect input fraction and convergence index measures, making quantitative comparisons unreliable. Furthermore, we suggest a principled way to analyse rabies derived connectivity data by taking advantage of the starter vs input cell relationship that we describe and validate across independent datasets.
Indeed, the advent of automated pipelines for registration and cell counting in whole-brain images is relatively recent  [14, 16, 17] , and without these pipelines, whole-brain cell counting for large datasets was an extremely time-and labour-intensive process. In addition, various methods are used to analyze rabies tracing experiments. Metrics used in previous studies include input fraction (the inputs count per area relative to either the total number of input cells in the brain, sometimes referred to as total input fraction  [15, [18] [19] [20] [21] , or relative to the number of input cells in a given area  [15, 22]  or layer  [23] ), and the convergence index, also called input connection strength index or input magnitude (area inputs count normalized by number of starter cells  [18, 20, [24] [25] [26] ).
The disparity in methods and terminology used to report the properties of connectivity maps complicates direct comparisons across various studies. Here we set out to explore the relationship between the number of starter and input cells. We generated a comparatively large dataset with a broad range of starter cell numbers in layer 5 of mouse visual cortex. We then used descriptive statistics, model selection and numerical modelling of connectivity to explore the data, and compare various analysis approaches. We find that the relationship between number of the starter cells and number of the labelled input cells is non-linear, and that the range of starter cells differently affects some of the metrics commonly used to analyze connectivity maps. Using these results we suggest principles for experiment design and appropriate analysis methodologies to use depending on the data available and on the desired information to be extracted.
Results We carried out rabies tracing experiments initiated in layer 5 pyramidal neurons (L5PN) in mouse primary and secondary visual cortex. We followed previously described protocols  [3]  using the G-protein deleted, EnvA pseudotyped version of CVS-N2c rabies virus  [6]. To achieve a wide range of starter cell numbers, we used different labeling strategies. We took advantage of varied cell densities in Cre driver lines labeling L5PNs, or through retrogradely labeling L5PNs by injecting retrograde AAV viruses to their projection targets. In addition, we also varied the volume of the injected helper viruses. Our dataset is thus uniform in terms of cell-type (L5PN), yet contains sufficient heterogeneity in terms of sub-types and starter areas to test comparative approaches. Whole brains were imaged using 2-photon tomography, then registered, segmented and annotated according to the Allen Common Coordinate Framework (CCFv3,  [27] ) using the open source brainreg software  [17].
Starter and input cells were detected and classified using the open source software cellfinder  [28]. Area-wise cell counts for individual experiments are reported in S1 Text. We use the following nomenclature throughout. Starter cells (also called postsynaptic, host or target cells in the literature) are neurons for which rabies tracing was initiated, and their count is denoted by n s. Neurons labelled after the transsynaptic jump, and thus considered first-order presynaptic to starter cells, are called input cells and their count in the entire brain is denoted by n i. Starter and input areas are brain areas defined according to the CCFv3 containing starter or input cells, respectively. Brain area names and abbreviations are from the Allen Common Coordinate Framework (CCFv3,  [27] ).
The relationship between input and starter cells is non-linear and is best described in log scale We took advantage of the broad range of starter cell numbers and the large number of experiments in our dataset to investigate the nature of the n i vs n s relationship. First, we fitted wholebrain n i vs n s data with linear, quadratic, exponential and power-law functions as well as a growth model that describes the labeling of input cells at a given rate, and considering a maximum number of input cells that can be labeled (Fig  1A ). We then performed model selection using the Akaike Information Criterion corrected for small sample sizes (AIC c )  [29]. From the model selection procedure, the power-law model and growth models are the best candidates (Table  1 ). However, residuals showed strong heteroscedasticity when standardized residuals were plotted against the fitted values (Fig  1B ).
Consequently, we used log-transformed n i and n s data for further analysis. To test the generality of our conclusions, we performed model selection and error model analysis on datasets from published studies performed on a range of cell types and starter brain areas, using different imaging and cell counting approaches as well as employing various rabies virus strains and G-proteins  [13-15, 18, 19, 22, 25, 32-36]. The analysis revealed statistical support for the power-law and growth models for untransformed n i vs n s data, a linear model for the log-transformed data, and for the log-normal error model (S1 Fig, Tables  1 2 3 ) Table  1. ΔAICc for untransformed data. External datasets are from  [13-15, 18, 19, 21, 22, 25, 32-36]. For easier comparison, we show the ΔAICc values, calculated as the difference between the AICc value for each model and the lowest AICc value per dataset.
Lower AIC values indicate a better-fit model, and a model with a ΔAIC (the difference between the two AIC values being compared) of more than -2 is considered significantly better than the model it is being compared to. across most datasets. Therefore, the growth of labelled inputs with the number of starter cells follows broadly the same statistical rules independently of experimental conditions and is not specific to our dataset. Furthermore, this generality allowed us to investigate whether rabies strains may affect the parameters of the n i vs n s relationship. We used a linear classifier to determine whether the rabies strain used could be identified using the log(n s ), log(n i ) and the broad class of starter cells (pyramidal or interneuron) as features. The classifier could predict the rabies strain with 82% accuracy (S2 Fig) and revealed a general trend where brains labelled using the CVS-N2c rabies strains displayed higher input cell counts for a similar number of starter cells, consistent with a higher trans-synaptic yield of the CVS-N2c strain  [6].
Statistical differences in area input fractions between the low-and high starter groups were found in 5 out of the 19 input areas analyzed (S14A Fig). In addition, data from the low starter group showed a much larger variability than the high starter group. Therefore, comparisons using the area input fraction measure should only be used for data sets where starter cell numbers are above the breakpoint value for which the area input fraction vs n s relationship reaches its horizontal asymptote. Convergence index suffers even more drastically from larger variability in the low-starter group (S14B Fig) and using it to compare datasets with different n s is inappropriate by construction.
Relative connection probability determines the behaviour of area input fractions at low starter numbers In order to directly assess how connectivity parameters affect the behaviour of the area input fraction vs n s relationship, we simulated a network with 5 input areas using the probabilistic model introduced in S3 When only p was varied between input areas, the area input fraction varied with n s , as in the experimental dataset (Fig 5A  -5D ). The influence of p on area input fractions vs n s is more apparent for low starter cell numbers: areas with the highest connection probability are overrepresented in terms of their relative input proportion, while areas with lowest connection probabilities are under-represented (Fig 5D  and 5E ). For high n s , there is little dependence of area input fractions on p, since the area input fraction vs n s relationship has reached its horizontal asymptote (Fig 5D  and 5F ).
This can be summarized by the decline of the relative input fraction between high and low n s as a function of normalized p (Fig  5G ). In contrast, when only N i was varied across input areas, the area input fraction for each area was constant across the starter cell range (S17 Fig). Consequently there was no difference in area input fractions between low and high n s datasets (S17C and S17F Fig). When both N i and p were varied, the interaction remained essentially the same (S18 Fig) , i. e. p drove the difference between low and high n s datasets. Finally, in order to obtain an estimate of N i and p for input areas in our dataset, we fit the data using the likelihood function of the probabilistic model used for simulations. The fitting procedure was performed using maximum likelihood estimation, and returns fitted parameter values for N i and p (Table  8 ).
Cell counting Some of the external datasets analysed in S1 Fig were quantified by counting labelled cells in separate brain slices. This approach can introduce bias and be a further source of variability. While advances in design-based stereological methods have improved cell number estimations from tissue slices  [41] , they typically rely on uniform cell densities and are rarely applied when quantifying rabies tracing data. To illustrate the problem, we have run numerical simulations using a range of biologically relevant cell densities (see methods). Counting cells in every slice or every 2 nd , 3 rd or 4 th slice consistently resulted in varying degrees of systematic overestimation (S20 Fig). Furthermore, the variance between individual experiments was considerable, especially toward low cell densities.
To further illustrate the problem using real data, we have virtually sectioned two brains from our dataset and counted cells in every 4 th slice. The estimated whole brain cell counts varied considerably depending on slice sampling. Importantly, estimates greatly differed from the cell counts obtained by whole-tissue 3D cell counting (Table  9 ). These results argue against estimating cell numbers by counting cells in sectioned tissue, especially when only sampling some slices. The imprecision introduced may underlie some of the model selection uncertainties in S1 Fig. To achieve unbiased, accurate cell counts, cell counting in the whole 3D volume of interest is essential. While automated 3D cell counting can deal with very large datasets and offers consistent results, systematic cell detection biases which scale with the label density cannot be excluded (e.
g. under-detection at input areas with extremely high cell densities). However, this is unlikely to manifest in our results as model selection analysis is consistent across input areas with different label densities. In addition, datasets from multiple laboratories and acquired through different imaging and cell counting methods show the same relationship. Interestingly, a dataset generated with no explicit cell counting, but rather quantifying projection volume by counting pixels reaching a certain fluorescence threshold (Allen Institute), shows a different residuals distribution (normal). Yet the n i vs n s relationship is still best described by a power-law relationship. The difference in residuals distribution analysis for this dataset may be expected through a presumably sub-linear increase and saturation in the projection volume with high cell densities.
Additionally, an over-estimation for areas of low cell density is similarly expected, as the inclusion of neuropil has a disproportional effect at low cell densities. Our results generalize well across external datasets, but it is important to note that the starter areas in all datasets explored here are cortical (except  [32, 34] , which are from the olfactory bulb and amygdala, respectively). It remains to be determined how the n i vs n s relationship generalises to profoundly different neuronal structures, such as the cerebellum, striatum or the spinal cord.
Model selection and error structure Metrics used in previous studies implicitly assumed linear scaling between the number of input and starter cells. We performed model selection analysis on our data (on whole brain inputs or area-defined inputs) and a range of external datasets, to compare the linear relationship with a range of candidate models that could describe the increase in number of inputs cells with increasing number of starter cells. For most datasets, the models with the lowest AICc values were the power-law and growth models (Table  1 ). This model selection analysis is limited by the data (both the number of observations, and the range of starter cells) and by the list of candidate models provided. For instance, although our dataset does not present any obvious saturation of the number of labelled inputs, one would expect a saturating regime to be reached if enough starter cells were labeled.
Therefore this analysis does not aim at identifying a "true" underlying model, but at defining how to fit the data for consistent descriptive analysis. However, it does show that the linear model is consistently outperformed by competing non-linear models. In addition, it is apparent that the n i vs n s relationships show a large variance in the data, and a skewed residuals distribution. The error structure analysis supports log-transformation of the data, which results in more normally distributed residuals (Fig 1B  and 1D ). We thus performed further analysis after log-transformation. Model selection on log-transformed data shows that a linear model provides a good description of the datasets. Although this doesn't represent the underlying model in log scale, it appears the range of n s sampled corresponds to a linear part of the log-transformed dataset.
Do rabies strains impact extracted parameters? In recent years, viral tracing tools have considerably improved, thanks to the introduction of engineered glycoproteins  [42]  and more efficient rabies strains  [6]  that increased efficiency of synaptic transfer and reduced neuronal toxicity. One concern would be that using different rabies strains could lead to different n i vs n s relationship models. We analysed a large number of datasets comprising data from two different rabies strains (SAD-B19 or CVS-N2c), a large range of starter cell types and various starter areas. For the overwhelming majority of the datasets, our model selection analysis returned the same qualitative distribution (Tables 1-3 and S1 Fig). We however did observe quantitative differences in log(n i ) vs log(n s ) relationships depending on the rabies strain used, with a higher intercept in the fit of the log(n i ) vs log(n s ) relationships for CVS-N2c.
This is consistent with the shifts in distributions of both starter and input degrees observed when varying N i and p using the probabilistic model (S8 Fig). Using these observations we can attach a biological meaning to some of these parameters. The measured y-intercept translates into the number of input cells to a single starter cell-a measure otherwise only obtainable by single-cell initiated rabies tracing  [1, 2]. Fitting our dataset yields a y-intercept of 3. 2, thus we estimate *1860 rabies labelled input neurons per starter cell. While the efficacy and specificity of transsynaptic rabies transmission present significant unknowns discussed elsewhere  [43] , this is not a wholly unreasonable estimate given a recent study reporting 7500 synapses for L2/3 pyramidal cells  [45]  and assuming multiple synapses per connection.
Conclusion and recommendations When planning rabies tracing experiments, one should first consider the required conclusions to be drawn. In general, starter cells should always be counted and a significantly larger number of samples, in the order of tens, should be planned. Starter cell numbers should ideally cover a broad range (from single digits to thousands), with denser sampling in the low n s. In addition, the starter cell population should be as homogeneous as possible. Experimental data thus obtained can be subjected to model selection, and used to estimate biologically pertinent parameters, such as y-intercept and N i. If only a small number of experiments are feasible, one should aim for a large number of starter cells where area input fractions have less variability across experiments and are close to a steady state.
Data acquisition and analysis Brain samples were embedded in 4-5% agarose (Sigma-Aldrich: 9012-36-6) in 0. 1M PB and imaged using serial two-photon tomography  [47, 48]. Eight optical sections were imaged every 5 μm with 1. 2 μm x 1. 2 μm lateral resolution, after which a 40 μm physical section was removed using a vibrating blade. Excitation was provided by a pulsed femto-second laser at 800 or 930 nm wavelength (MaiTai eHP, Spectraphysics). Images were acquired through a 16X, 0. 8 NA objective (Nikon MRP07220) in three channels (green, red, blue) using photomultiplier tubes. Image tiles for each channel and optical plane were stitched together using the open-source MATLAB package StitchIt (https://github. com/SainsburyWellcomeCentre/StitchIt). For 3D cell detection the open-source package cellfinder  [28]  was used.
Registration to the Allen CCFv3  [27] )and segmentation was done using the brainreg package  [17]. Cell coordinates were downsampled to 10 μm to match the resolution of the Allen CCFv3 space and the number of cells was counted for each segmented area. To reduce the occurrence of false positives, the hindbrain areas (HB) were removed from the whole brain cell counts, as these areas are known not to project directly to the neocortex. Area-wise cell count data were imported into Python 3. 6 and all further analysis performed using custom scripts. In the following Methods sections, we refer to n s as the number of starter cells, and n i as the number of input cells, or n i, A for the number of input cells in area A. For fitting relationships, the explanatory variable x can refer to n s or log(n s ), and the dependent variable y can refer to n i or log(n i ).
Analysis of residuals distribution If the residuals from a fit of the untransformed dataset follow a normal distribution, further analysis should be performed the untransformed dataset. If residuals follow a log-normal distribution, it is more appropriate to perform further analysis on log-transformed data. We used the method described in  [31]  to determine the error structure of the dataset. Briefly, we calculated the likelihood that the data is generated from a normal distribution with additive error: L norm ¼ Y n i¼1 1 ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi 2ps 2 NLR p exp À ðy i À a NLR x b NLR i Þ 2 2s 2 NLR ! " # and the likelihood that the data is generated from a lognormal distribution with multiplicative error: L lognorm ¼ Y n i¼1 1 ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi 2ps 2 LR p exp À ðlogðy i Þ À logða LR x b LR i ÞÞ 2 2s 2 LR ! " # where n is the sample size.
Effect of the number of starter cells on area input fraction Area input fractions were defined as the ratio between the number of cells in each input area in ipsilateral side, and the total inputs counted in the same brain. We use a Chow test  [37]  to test for structural breaks in the slope of the area input fraction vs starters relationships. Multivariate linear regression analysis was used to compare the relative effect of starter cell numbers, starter cell locations, and starter cell genotype, on area input fractions for all regions of interest. The regression models were defined using the ols function from the statsmodel Python package. Models with different combinations of the predictors were also assessed. Statistical significance of the models were assessed by an F test and all p values corrected for multiple comparisons using the Benjamini-Hochberg method.
Classification of rabies strains across datasets We used all datasets with starter cell identified as Pyramidal cells or interneurons and input cells quantified as cell counts. Classification was performed using the sklearn package. Rabies strain was used as the target of the classification, and cell type, starter numbers and input numbers used as features. Starter and input numbers were normalized using MinMaxScaler. Linear support vector classification was performed using the LinearSVC classifier. We used stratified k-fold cross-validation with k = 4. Briefly, the data was randomly divided into k subsets, so that the created folds preserve the distribution of classes observed in the complete dataset. Each subset was iteratively used as the test set while the other k-1 subsets were used for training.
Acknowledgments We thank  Troy Margrie  and  Molly Strom  for viral constructs,  Rob Campbell ,  Charlie Rousseau  and  Adam Tyson  for help with data acquisition and analysis of rabies tracing experiments,  Gavin Kelly  for help with model selection,  Marco Beato ,  Jonny Kohl  and  Petr Znamenskiy  for comments on the manuscript, and all colleagues who shared their data in S1 Fig. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.