dataesr/scientific-paragraphs-categorization huggingface dataset:
Macroscopic and myelinic modifications of brain autopsies The autopsy of Patient IT was limited to brain and upper cervical spinal cord. The brain was not weighed, but the examination confirmed the data obtained by MRI, with  atrophy of the cerebellum, particularly of the vermis. The corpus callosum was also thin. The autopsy of Patient BG-2 was also limited to the brain, which weighed 950 g, and to the thoraco-lumbosacral spinal cord. Macroscopic examination revealed mild atrophy of the frontal lobe, severe thinning of the corpus callosum, dilatation of the ventricular system and a greyish aspect of the white matter, especially in subcortical areas. There was some depigmentation of the substantia nigra, a slight atrophy of the pons and atrophy of the pyramids in the medulla oblongata as well as a thinning of the spinal cord.
In agreement with this hypothesis, no immunolabelling with antibodies directed against spatacsin developed and characterized by us  (Murmu et al. , 2011) , was obtained in brain paraffin sections from the present cases (data not shown). No Lewy bodies, neurofibrillary tangles, senile plaques or intranuclear inclusions were observed either by histological (haematoxylin and eosin, Masson's trichrome or Bodian silver impregnation) or immunohistochemical staining (phosphorylated alpha-synuclein, hyperphosphorylated tau protein AT8 and amyloid-b 4G8 labelling). In addition, no anti-FUS immunoreactivity was observed, neither in spinal motor neurons nor in the remaining neurons of the hypoglossal nucleus, in contrast to positive anti-FUS inclusions observed in a control case with frontotemporal dementia carrying a FUS mutation (data not shown).
Funding This work was supported by the  Agence Nationale de la Recherche  (to G. S. ), the  Verum Foundation  (to A. B. and G. S. ), the  Roger de Spoelberch Foundation  (to A. B. ), the  ERA-Net for Research Programmes on Rare Diseases (Erare "Neurolipid" , to G. S. ), the  European Union  with the  European Research Council (ERC, Starting  grant No. 311149  to F. D. ) and the  Seventh Framework Programme  ( FP7, Omics call , to A. B. ). This study benefited from the  programme 'Investissements d'avenir'   ANR-10-IAIHU-06  and  ANR-11-INBS-0011-NeurATRIS  ( Translational Research Infrastructure for Biotherapies in Neurosciences ).
Experimental Protocol The experiment took place during two ESA campaigns of parabolic flights (186 parabolas) on board the CNES Airbus Zero-G aircraft. Because no blind pulling data exist in the literature, it was necessary to compare the distance estimates during both blind pulling and blind walking with the same subjects. These tests were done days in advance of the flights in a stress-free condition comparable to the laboratory environment. One to three days prior to the parabolic flight, distances estimates were measured in 1 g during blind walking on the ground on an unmarked roadway. The same tennis ball as used in the experimental setup in the aircraft was placed on the road at various distances and the subjects were instructed to briefly look at the ball, close their eyes and walk to the ball.
0% (SE 0. 4%). During the blind-pulling tests, distances were even more underestimated (mean 16. 2%; SE 1. 1%) (Fig  2 ). Preflight comparison between the perceived distances estimated using blind walking, blind pulling, and verbal reports for each target distance (true distance). Mean ± SE of 9 subjects. The continuous lines are the fits by the leaky integration model described in  [16]. k is the gain and a is the spatial decay constant; the dashed line indicates veridical performance (k = 1; a = 0). Individual data are reported in S1 Table. The distance estimates taken during the flights using verbal reports and blind pulling were each analyzed using a 10 (targets) x 3 (gravity; 1 g, 0 g, 1. 8 g) repeated-measures ANOVA. For verbal reports, there was a significant effect of target distance [F (9, 240) = 650, p < 0.
Mammals show a wide range of brain sizes, reflecting adaptation to diverse habitats. Comparing interareal cortical networks across brains of different sizes and mammalian orders provides robust information on evolutionarily preserved features and species-specific processing modalities. However, these networks are spatially embedded, directed, and weighted, making comparisons challenging. Using tract tracing data from macaque and mouse, we show the existence of a general organizational principle based on an exponential distance rule (EDR) and cortical geometry, enabling network comparisons within the same model framework. These comparisons reveal the existence of network invariants between mouse and macaque, exemplified in graph motif profiles and connection similarity indices, but also significant differences, such as fractionally smaller and much weaker longdistance connections in the macaque than in mouse.
The latter lends credence to the prediction that long-distance cortico-cortical connections could be very weak in the muchexpanded human cortex, implying an increased susceptibility to disconnection syndromes such as Alzheimer disease and schizophrenia. Finally, our data from tracer experiments involving only gray matter connections in the primary visual areas of both species show that an EDR holds at local scales as well (within 1. 5 mm), supporting the hypothesis that it is a universally valid property across all scales and, possibly, across the mammalian class.
Results We first give a schematic description of EDR-based network models (Fig  1 ) before developing a formal methodology for comparing EDR model graphs with experimentally obtained graphs, thereby allowing a quantification of the predictive power of the EDR network model for a given brain. This sets the stage for empirical measurements in the mouse brain, which are required for the construction of a mouse EDR model (Fig  2 ) and to examine how well the mouse EDR graphs fit with selected local and global mouse network properties obtained from empirical data (Fig  3 ). We next identify the core-periphery organization in the mouse network and show that it is well captured by the mouse EDR model. The following section is dedicated to a comparison of the capacity of the mouse and macaque EDR models in predicting empirically measured motif distributions (Figs  5  and 6 )  [38].
EDR-Based Network Model of the Cortex To what extent does the EDR, as a connectivity constraint, determine the properties of the interareal network? To address this issue, one needs (i) a family of EDR-based network models and (ii) a method of comparison between the model-generated networks and the experimental data network. The exponential decay rule ~e-λd in the macaque was obtained from collating all the labeled neurons (over 6. 4 million) following tract tracing experiments in different areas and constructing an interareal distance matrix, the latter estimated as the distances between the area barycenters through the white matter (WM), along the shortest paths. Here axonal p(d) should be interpreted as an average property (see Fig  1A ), the probability that an axonal bundle projects to a distance d, independently of the specific functional nature of the areas.
At this level of description, the strength of the connection between areas, expressed as the fraction of labeled neurons (FLN), depends uniquely on their geometrical separation. Thus, the network is viewed as a spatial, directed, and weighted graph dependent on the matrix D = {d ij } of interareal distances d ij. We emphasize here that the EDR arises from the estimated probability distribution of axon lengths. Although the strength-distance relation is consistent with the EDR, the probability distribution of axons lengths provides a more compelling demonstration of the property and leads naturally to the parametric EDR model described below. The probability density function, q(d), of the distances in the matrix D is typically a unimodal distribution (Fig 1B ), which, when combined with the exponential decay p(d), leads to a log-normal distribution of edge weights, confirmed by the empirical FLN data  [4, 39].
Network Fitting and Comparison We denote the data network obtained from the experiments by G exp (e. g. , for the macaque we use G mac exp , and for the mouse G mac exp ). Our goal is to compare the properties of the EDR model networks with the properties of G exp. Since the model networks are only based on distance-dependent connection probabilities, one cannot expect perfect agreement (edge-by-edge) with the biological connectivity graph G exp , however, if the distance rule is a strong determinant of the interareal network, the model graphs should be statistically similar to G exp. The comparisons are performed via parameter matching of network properties  [4] : for a given network property P, the interareal distance matrix D and parameter λ is used to generate a large ensemble of EDR graphs G EDR ðlÞ.
By varying λ we determine the value λ P via minimizing the deviation |P(G exp ) -hP(λ)i|, with the average hÁi taken over at least 10 3 EDR graph realizations from G EDR ðlÞ. Thus the model parameter is determined so that the average of P in the model is as close as possible with the value of P observed in the data network. We then compare the fitted value λ P with λ exp , the decay rate obtained directly from the experiments. If the two are close, then the EDR is a strong determinant for the measure P of the cortical network. Thus, the extent a particular measure in the EDR model and in the data network agree, i. e. , |P(G exp ) -hP(λ P )i| with respect to the same comparison with the CDR model, i. e. , with |P(G exp ) -hP(λ = 0)i|, expresses the degree to which the EDR influences that particular measure in the cortical network.
We then applied the network analysis described in the section above to the largest available edge-complete graph (the status of connectivity between all pairs of nodes is known) of 33 areas in one hemisphere of the mouse neocortex  [5, 6] , denoted by G mus exp from here on (S1 Fig). This mouse dataset contains 719 directed pathways and has an interareal network density of ρ mus = 0. 68, similar to that reported in the macaque. Fig  3  shows the proximity of l mus P obtained using the parameter matching method to the decay rate l mus exp for several network measures including the number of area pairs connected uni-(M 1 ) or bidirectionally (M 2 ), 3-motifs, clique distributions and the second largest eigenvalue of the symmetrized form AA T of the adjacency matrix A. These measures have been selected in part because they probe graph properties from local to global scales, and are of varying complexity.
Comparing Mouse and Macaque Cortical Networks within a Common Spatial Template Elsewhere we have demonstrated that a quantitative measure of the similarity of the connectivity profiles of target cortical areas decreases in a regular fashion with increasing distance between them, i. e. , the closer two target areas are, the more their source areas overlap  [4, 33]. We have also shown that changes in similarity reflect the functional layout of the cortex  [33] , and thus it is natural to compare the behavior of this measure between the mouse and macaque. A similarity index  [4]  can be defined for both incoming (in-link similarity) and outgoing (out-link similarity) connections. In order to compare macaque and mouse similarity indices, we focus here on the incoming connections, as those are the ones fully specified for all the injected areas in the macaque dataset.
Imaging and Neuronal Counts To survey the injection site and the distribution of labeled neurons across cortical areas, sections were wet-mounted in PB and imaged in St. Louis under a dissection scope equipped for UV-and red-fluorescence illumination. For plotting DY labeled neurons, the sections were permanently mounted onto glass slides and stored at 4°C. The distribution of DY-labeled neurons was analyzed in Bron. Plots of DY neurons were made at 20× under a fluorescence microscope equipped for UV illumination (excitation: 387-398 nm, emission: 435-475 nm), using the Mercator software package running on ExploraNova technology. Labeled neurons were contained in 12-16 sections per hemisphere. Digital charts of the coordinates of DY labeled neurons across each section were stored in the computer.
Automated Processing Automated processing was performed using in-house software, written in Python. For each case, the positions of labeled neurons inside neocortex, but outside the injected area (i. e. , extrinsic neurons) were extracted in digital format, The fraction of labeled neurons per area (FLN) was estimated as the number of labelled neurons extrinsic to the injected area expressed as a fraction of the total number of labeled neurons in the cortical hemisphere  [39]. Unlike the template matching procedure used in previous studies  [5, 6]  we parcellated each cortex individually based on multiple markers expressed across different tangential sections. In a stepwise procedure we first used density differences in the expression of PV-tdT-labeled cell bodies and processes to delineate borders of single areas such as V1, S1, S2, Au, PD, UF, PV, GU, ORBI, MM.
Datasets The Allen Brain Institute (ABI) dataset was collected on their website, offered as link in their original research publication  [5]. The University of Southern California (USC) matrix was, on the other hand, extracted directly from their original article  [6]. The ABI mouse atlas possesses 40 isocortical areas according to their Supplemental Table  1. Out of these 40 areas, 2 did not correspond to any line or column in the data as structured in S1 Fig (i. e. the connectivity matrix). An additional 4 areas were not considered as primary target of an injection by the authors, leading to our decision to exclude them from our analysis. We then extracted from the ABI data a 34 × 34 weighted and directed connectivity matrix. The larger USC dataset has a finer grained parcellation than that of ABI, although based on the same fundamental scheme.
We contracted the USC final matrix down to a level of 42 × 42 by merging areas together in both rows and columns so as to obtain a squared matrix. At this point, the ABI and USC matrices had 33 areas in common, which corresponds to 97% and 79% of their full respective matrices. A similar parcellation scheme was extracted out of the two datasets, allowing complete, connection-by-connection comparison between the two matrices, see S1 Fig for the final connectivity matrix.
Technical Considerations The database in the mouse has been generated following tracer injections in all cortical areas. The macaque data, however, was obtained from 29 injections using a 91-area atlas. Because in macaque we are using an edge-complete subgraph, the statistical features are predicted to reflect those of the, as yet unavailable, fully connected graph. However, the presently available dataset cannot give complete information on detailed areal relationships, such as for example the full membership of the cortical core.
Variance Comparisons of Distance Distributions Fig 7C shows the histograms of connection distances for mouse and macaque after normalization by the mean distance for each species. By construction, both distributions have mean equal to 1 and can be reasonably well described by truncated normal distributions. When fitting the distributions by maximum likelihood using functions from the truncnorm package  [77]  in R  [78] , the variance of the macaque normalized distances appears smaller than for the mouse data with a ratio of 0. 608. Is this significant? First, we examine this question with an Ftest on the ratio of variances. The test is two-sided because we do not specify a priori which variance is greater. This is a more conservative approach. The F-statistic is the variance ratio with of freedom (405, 527) giving a highly significant p = 1.
58 × 10 -7. The test assumes normality, however. To verify the conclusion, then, we performed a permutation test that does not make the normality assumption  [79]. In short, we permute the macaque and mouse labels a large number of times and recomputed the variance ratio for each new permutation. Under the null hypothesis that both distance distributions are the same, we expect a large number of variance ratio estimates on the permuted datasets that are more extreme than the variance ratio computed on the data. The p-value is computed from the proportion of ratio estimates more extreme or equal to the obtained value. For ratios, the definition of more extreme is based on the values that are less than the estimate and greater than its reciprocal. We include the ratio estimate from the dataset in the distribution of permutation estimates.
S7 Fig shows the value of the ratio of variances for 100,000 permutations of the two datasets. The vertical line indicates the value obtained from the data, which is lower than all of the other values of the permutation distribution, indicating that the obtained ratio is highly unlikely under the hypothesis that both distributions are the same. The p-value is indicated in the graph. The pvalue is smaller than 10 -5 , which is the resolution of the test for 100,000 permutations. Thus, the width of the distribution of distances for macaque is significantly narrower than that for mouse.
The choice of degree of penalization (or smoothness) is controlled by minimizing a criterion related to prediction error (i. e. , fitting some of the data and calculating the error on the remaining portion) called the un-biased risk estimator (UBRE) that is closely related to Aikake's Information Criterion (AIC). Like AIC, UBRE favors a model that maximizes the predictability of future rather than the actual data and serves to minimize the tendency to overfit the data. The fits were performed with functions from the mgcv package  [81]  in R  [78]. The estimates of the smooth curves for macaque and mouse are plotted in Fig 9C  and  9D macaque and mouse, respectively, with twice the estimated standard errors of the fits. To estimate the significance of the differences between the two estimates, we also fit the simpler nested model in which a single smooth curve described connectivity dependence with distance for both species.
Connectivity Similarity Indices The method used to compute binary similarity indices with macaque data has been described previously  [4]. Our published macaque database is made of 29 injected areas for a 91 parcellation scheme, thus giving a 91 × 29 connectivity matrix. In this context, only the in-degrees of injected areas are completely known, the out-degrees of source areas remaining incomplete. Therefore, if one wants to compare macaque and mouse using a degree-based binary similarity measure, one has to restrict oneself to in-degrees, in order to use complete data. For this reason, we detail here only the in-degree based similarity measurement calculations. The union of ABI and USC databases used here provides information about all 33 areas in terms of the in and out-going connections between 33 areas.
SzH was supported by the " Programme Avenir Lyon Saint-Etienne " of the  Université de Lyon  ( ANR-11-IDEX-0007 ), within the  Program "Investissements d'Avenir " operated by the  French National Research Agency (ANR)  http://palse. universite-lyon. fr/,and  Marie Curie Program European Union's Seventh Framework  ( FP7/2007-2013 ) No. PCOFUND-GA-2013-609102 ,  PRESTIGE  coordinated by Campus France http://www. campusfrance. org/en/prestige. MER was supported by the  UNESCO-L'Oreal National Fellowship "For Women in Science " http://www. fwis. fr/, by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No 668863 https://ec. europa. eu/programmes/horizon2020/, and in part by the  GSCE-30260-2015 "Grant for Supporting Excellent Research " of the  Babeş-Bolyai University  http://ubbcluj.
ro/. DCVE and LM were supported by grant  National Institutes of Health (NIH)   R01-MH-60974  https://grants. nih. gov. AB was supported by  National Institutes of Health (NIH)   R01 EY016184  https://grants. nih. gov and the  McDonnell Center for Systems Neuroscience  (to AB and RG) http://centerserv. wustl. edu/. ZT was supported, in part, by grant  FA9550-12-1-0405  jointly from the  US Air Force Office of Scientific Research (AFOSR)  and  Defense Advanced Research Projects Agency  (DARPA) http://www. darpa. mil/,and by grant No. HDTRA-1-09-1-0039  from  Defense Threat Reduction Agency  (DTRA) http://www. dtra. mil/. HK was supported by  ANR-11-BSV4-501  ( CORE-NETS ),  ANR-14-CE13-0033  ( ARCHI-CORE ),  ANR-15-CE32-0016  (CORNET) http://www. anr. fr and  LabEx CORTEX  ( ANR-11-LABX-0042 ) http://www.
Oxygenated Ringer's solution was continuously perfused at room temperature. The dendritic knobs of OSNs were visualized through an upright microscope (BX51WI; Olympus) equipped with a camera (DP72; Olympus) and a 409 water-immersion objective, numerical aperture 0. 8. An accessory lens in the light path achieved an extra 49 magnification. The green-fluorescent cells were visualized under fluorescence illumination. Superimposition of the fluorescent and brightfield images allowed identification of the GFP+ cells under bright field. Electrophysiological recordings were controlled by an EPC-10 amplifier combined with PATCHMASTER software (HEKA Electronik, Lambrecht, Germany). Perforated patch clamping was performed on the dendritic knobs by including 260 lM nystatin in the borosilicaterecording pipettes (Sutter Instrument, Novaco, CA, USA), which were filled with a solution containing (in mM): 70 KCl, 53 KOH, 30 methanesulphonic acid, 5.
Data analysis Unpaired t-tests were performed with PRISM software (GraphPad) to indicate statistical differences between the two populations of OSNs. Dose-response curves were fitted with Origin 9. 1 using the Hill equation [I = I max /(1 + (K 1/2 /C) n )] where I represents the peak of odour-induced response, I max the maximum response at saturating concentrations, K 1/2 the concentration when half of the maximum response was reached (EC 50 ), C the concentration of odorant, and n the Hill coefficient. Electrophysiology data analysis was performed using PATCHMASTER and IGOR PRO software. Averaged data are shown as mean AE SEM.
However, for the false-positive response to cyclohexanone for MOR256-17 and to heptanoic acid and 1-octanethiol for SR1 [discrepancies in which a heterologous system  (Saito et al. , 2009; Goldsmith et al. , 2011; Yu et al. , 2015)  detects a response that cannot be reproduced in native OSNs that express the unmodified OR from the endogenous locus] there is no obvious explanation. The only available in vivo data for MOR256-17 are with 2,3-hexanedione, in a strain of MOR256-17-IRES-tauGFP mice that has been generated independently  (Luxenhofer et al. , 2008) ; in mice exposed to 2,3hexanedione, there is an increase in the percentage of c-fos+ juxtaglomerular cells surrounding the GFP+ glomeruli  (Loch et al. , 2013). Why does the mouse olfactory system employ broadly responsive odorant receptors? MOR256-17 and SR1 belong to the same OR family, the MOR256 family.
Acknowledgements The mouse strain MOR256-17-IRES-tauGFP was generated in the laboratory of P. M. at  The Rockefeller University. M. K. thanks the late  Andreas Walz  for his kind help with targeting vector construction. P. M. is grateful for generous support from The  Rockefeller University  and the  Max Planck Society. X. G. is grateful for generous support from the  CNRS  and the  Conseil R egional de Bourgogne. B. T. and X. G. thank  Anne Lefranc  and the  Centre des Sciences du Goût et de l'Alimentation animal facility  for excellent animal care.
Materials. The experimental program ran on Mac Pro (Apple Inc. ) computers in the Matlab (Mathworks Inc. ) environment, using functions from the Psychophysics Toolbox  29, 30. All stimuli were presented on Asus VG278H monitors (1920 × 1080 pixels at 120 Hz), calibrated to allow linear manipulation of luminance. Luminance values ranged from 1. 6 cd/m 2 to 159 cd/m 2. Chin rests were used to maintain viewing distance at 76 cm.