dataesr/scientific-paragraphs-categorization huggingface dataset:
Absolute values for each sample were adjusted relative to the protein concentration. H&E staining and immunohistochemical analyses. Animals were intracardially perfused with 4% paraformaldehyde, and their brains were removed and weighed on P15 or P56. All brains were embedded in paraffin. Sections (5-μ m thick) were cut at 3 mm (striatal level) and 4 mm (hippocampal level) from the frontal tip. One section at each level from each animal was used in each histological analysis. The sections were stained with hematoxylin and eosin (H&E). The whole cerebral area, median thickness of the corpus callosum at the striatal and hippocampal levels, lateral ventricular index and cortical thickness at the striatal level were measured using ImageJ software (NIH, Bethesda, MD, USA). Lateral ventricular index values were calculated as follows: total area of the bilateral ventricles/whole area at the striatal level × 100  48.
Statistics. Comparisons among the three groups (control, sham, and MIUH groups) were assessed using one-way ANOVAs, followed by a Tukey-Kramer tests. Comparisons of two groups were assessed using  Student's t-tests. When analyzing semi-quantitative scores, comparisons of two groups were assessed using Mann-Whitney's U-tests. Temporal changes in blood flow, body weight, brain weight, and performances in the negative geotaxis and open-field tests were assessed using two-way repeated-measures ANOVAs followed by Bonferroni-Dunn tests. Females and males were analyzed separately. The comparison of gestation periods was made using a Wilcoxon rank-sum test. Differences were considered significant at P < 0. 05. All statistical analyses were performed using Statcel3 software (OMS Ltd. Saitama, Japan).
Acknowledgements This work was supported by  FY 2013 Research Exchange Program between JSPS  and  CNRS ,  JSPS KAKENHI  Grant Number  26860858 , and  Narishige Neuroscience Research Foundation. We thank  Manami Sone ,  Mari Furuta  and  Mutsumi Sakamoto  for excellent technical assistance,  Dr. Toshimitsu Hamasaki  for statistical analysis, and  Dr. Yukiko Gotoh  for data interpretation.
Image Processing All data was analyzed using custom-written software in Matlab. On each image, larvae were detected by (i) substracting the run's average image, (ii) applying a Gaussian filter (175 × 175 µm box, σ = 50 µm), (iii) thresholding and filtering objects smaller than 500 pixels (∼50% of a larva). To evaluate body curvature, we used an approach based on image moments inpired by the work of  Rocha et al. (2002)  for tracking articulated objects. We evaluated the level-1 BSP-tree to obtain two equivalent ellipses of the head and the tail of each larva at each time frame (Figure  1C  and Supplementary Video 1). The body angle α was defined as the angle between the major axis of the head ellipse and the radial direction. We then considered the intersection of both ellipses' minor axes as the center of curvature and computed the radius of curvature R as the average distance between the center of curvature and all larva's pixels.
Data Analysis All data was analyzed using software custom-written in Matlab. Swim bouts were automatially detected based one the curvature signal κ. We first computed σ κ the standard deviation of the derivative of the curvature over a sliding window of 20 ms, and divided it by a baseline trace obtained by taking the average of σ κ below the median value over a large sliding window of 400 ms. Swim bouts were localized when the normalized σ κ stood above a threshold of 5 for more than 40 ms. In each swim bouts the tail beats were automatically detected when the absolute relative curvature stood above a threshold of 0. 1. For each bout, the time at which the radius ρ(t) was minimal defined the impulse start t k. The radial position traces were then fitted on the 100 ms preceeding each bout by an affine function ρ(t < t k ) = v f t + c to extract the fluid velocity v f , and on the 300 ms following each bout (or less if there was another bout in this range) by: ρ(t > t k ) = ρ(t k ) + λucos(α)(1 -e -t/λ ) -v f t (1) where u is the impulse velocity and λ is the Stoke's drag damping time scale.
Acknowledgments We thank  R. Richardson ,  R. Humphries ,  V. Cortay , and  N. Doerflinger  for excellent technical assistance with probe synthesis and histology; and  Drs. J. L. MacDonald ,  H. Padmanabhan P. H. Özdinler , and  M. J. Galazo  for stimulating discussions and helpful suggestions regarding neocortical molecular evolution and primate tissue handling. This work was supported by grants from the  National Institutes of Health  ( NS41590 ,  NS45523  to J. D. M. and predoctoral  NRSA  fellowship  NS073136  to R. M. F); The Emily and  Robert Pearlstein Fund , The  Jane and Lee Seidman Fund , and the  United Sydney Association  to J. D. M. ; a  National Science Foundation Graduate Research Fellowship Program (GRFP)  fellowship to R. M. F. ;  LabEx CORTEX.
Statistical analysis Sample sizes for our experiments were determined according to our previous studies. Each experiment with mouse retinal explants or light-challenge retina is representative of at least 3 independent experiments. Graph Pad Prism 6 (GraphPad Software) was used for data analysis and graphic representation. Grubb's test was used to determine outliers. All values are reported as mean ± SEM. Statistical analysis was performed by one-way Anova analysis of variance followed by Bonferroni or Dunnett's post-test (multiple comparison of groups with equal variance and normal distribution), Kruskal-Wallis followed by a Dunns post-test (non-parametric multiple comparison of groups) or Mann-Whitney U test (non-parametric 2-group experiments) for comparison among means depending on the experimental design.
Acknowledgements We would like to thank  Stephane Fouquet  and  David Godefroy  from the plateforme d'imagerie of the  Institut de la Vision (IdV)  for invaluable assistance in confocal microscopy. We are grateful to and  Celine Nouvel Jaillard (IdV)  and to Claire Mae ¨lle Fovet et  Joanna Demilly  ( MIRCen platform, CEA/ INSERM, Fontenay-aux-Roses, France ) for organizing and giving us access to cynomolgus macaques post-mortem retina for retinal explants. This work was supported by grants from  INSERM ,  ANR  blanc ( MACLEAR ,  ANR-J15R365 ),  Labex Lifesenses, Carnot , and by  Association de Prevoyance Santede ALLIANZ. Dr. Craft  is the inaugural endowed  Mary D Allen Chair  in Vision Research,  Doheny Eye Institute , and supported, in part, by  NIH   EY015851 ,  EY03040  (DEI), and  Research to Prevent Blindness (USC Ophthalmology ).
In accordance with the cortical population response, the rise of individual learning curves was significantly shorter (192 ± 28 trials, n ¼ 12 mice), when the 2 s 60-85 dB white noise up-ramp was the S À compared with the opposite situation (310 ± 56 trials, n ¼ 12 mice, P ¼ 0. 0046, Kolmogorov-Smirnov test; Fig. 7g ). Note that the individual learning curves for this task typically display a delay period during which performance stays at 50% (licking occurs both on S þ and S À sounds) followed by a learning phase, in which mice start to avoid the S À sound  42  (Fig. 7f ), as observed in many learned behaviours  43. Hence, we measured rise duration on each individual curve from the end of the delay period to the end of the learning phase. Taken together, this data indicates that the 2 s up-ramp is behaviourally more salient than the down-ramp both in its initial and terminal phase as predicted by our cortical activity measurements (Fig.
Reflected light was collected at 20 Hz by a charge-coupled device camera attached to a macroscope. The focal plane was placed 400 mm below superficial blood vessels. A custom-made Matlab program controlled image acquisition and sound delivery. We acquired a baseline and a response image (164 Â 123 pixels, B3. 7 Â 2. 8 mm, image shown in Supplementary Fig. 1  are cropped around the sound responsive area) corresponding to the average images recorded 3 s before and 3 s after sound onset, respectively. The change in light reflectance (DR/R) was computed then averaged over the 20 trials for each sound frequency. A 2D Gaussian filter (s ¼ 45. 6 mm) was used to build the response map (Supplementary Fig. 1 ). Sounds were trains of 20 white noise bursts or pure tone pips (4,8,16 and 32 kHz) separated by 20 ms smooth gaps.
Data analysis. Data analysis and modelling was performed with custom-made Matlab and Python scripts available upon request, as well as the data sets. Only recordings performed within auditory cortex as assessed with intrinsic imaging were included in the imaging. Sample size (B4,000 neurons) was chosen to obtain a representative sampling of auditory cortex as assessed in a previous study  22. All images acquired during a session were registered by horizontal translation to a template image to correct for motion artefacts (all sessions with visible z motion were discarded). Regions of interest were then manually selected on the whole cell bodies of visually identifiable neurons 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.
Clustering was used to organize the imaged neuronal responses. Due to the large variability observed in many neurons, this analysis is not exhaustive, but rather aims at identifying principal classes of responses within our data set. Clustering was performed across the 13 imaging sessions, in which we played both white noise and 8 kHz harmonic sounds (3,657 neurons). Response traces for all sounds were smoothed using a Gaussian filter (s ¼ 31 ms). Before clustering, we selected significantly responsive (assessed by testing for a difference of the pooled responses to all stimuli against their baseline using a paired Wilcoxon signed-rank test, Po0. 05) and selective neurons (significant modulation by one of the stimuli, Kruskal-Wallis test, Po0. 05) neurons. For the 2,343 neurons that passed both tests, the SNR was calculated as R dt rðtÞ j j h i trials À Á = ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi R dth rðtÞ À rðtÞ h i trials À Á 2 i trials q.
Acknowledgements We thank  Y. Fregnac ,  S. Rumpel ,  S. Ostojic  and  E. Harrell  for comments on the manuscript,  S. Sikiric  ´and  L. Franc¸ois  for help with behavioural and imaging experiments. This work was supported by the  Agence Nationale pour la Recherche (ANR  ' SENSEMAKER '), the  Fyssen foundation , the  DIM 'Region Ile de France' , the  Marie Curie Program  ( CIG 334581 ) and by The  International Human Frontier Science Program Organization  ( CDA-0064-2015 ).
, 2014). (Buchan et al. , 2013). High-confidence protein binding sites are shown in orange lines. (C) Multiple alignment of ACAD9 and VLCAD (highlighted in pink) orthologues around ACAD9 residues 383 to 561, based on CLUSTALX  (Larkin et al. , 2007)  and edited with BioEdit  (Hall, 2011). This regions shows the highest sequence variability between ACAD9 and VLCAD homologs and is predicted to correlate with the folding of an external 35 residue helix (shown in gray) that seems to be absent in the VLCAD crystal structure (PDB code 2UXW,  McAndrew et al. , 2008)  and which might represent a specific interaction domain site unique for ACAD9. Residue numbering according to human ACAD9 sequence. h, human; m, mouse; x, African frog; d, zebrafish. Furthermore, depletion of TMEM126B leads to accumulation of the Q module-ND1 intermediate  (Guarani et al.
Sound waveforms convey information largely via amplitude modulations (AM). A large body of experimental evidence has provided support for a modulation (bandpass) filterbank. Details of this model have varied over time partly reflecting different experimental conditions and diverse datasets from distinct task strategies, contributing uncertainty to the bandwidth measurements and leaving important issues unresolved. We adopt here a solely datadriven measurement approach in which we first demonstrate how different models can be subsumed within a common 'cascade' framework, and then proceed to characterize the cascade via system identification analysis using a single stimulus/task specification and hence stable task rules largely unconstrained by any model or parameters. Observers were required to detect a brief change in level superimposed onto random level changes that served as AM noise; the relationship between trial-by-trial noisy fluctuations and corresponding human responses enables targeted identification of distinct cascade elements.
For example, under some stimulus and experimental conditions only parts of the dataset conformed to low-pass characteristics  [6, 15, [21] [22] [23]  but not others  [16, 24, 25]. Plausible accounts of these apparent inconsistencies involved shifts in task strategies and/or decision statistics  [15, 21] , whereby listeners would rely on different cues under conditions associated with different portions of the dataset and/or subtle stimulus alterations  [26] [27] [28]. Strategy shifts of this kind are relatively common  [29]  (e. g. off-frequency listening  [30] ). Therefore, our first goal was to assess the extent to which endogenous adaptive strategies can influence the measurements of the modulation filter when no stimulus and/or task-related factors are concomitantly modified. Specifically, we sought to adopt a single stable measurement approach throughout (the reverse correlation method), reasoning that systematic changes in the subjects' detection strategies would be unveiled as a dynamic evolution of the filter estimates during the experimental session  [31] [32] [33].
L 1 /L 2 estimates via cascade characterization Cascade filters and psychophysical kernels are different classes of objects. Cascade filters are filtering components of a hypothesized cascade model; for the L 1 NL 2 cascade described previously, they cannot be estimated directly from data via simple rules. They can, however, be estimated indirectly via the psychophysical kernels. Psychophysical kernels are data descriptors computed directly from the raw data using simple rules (see above); in this sense, they are not dissimilar from simply computing a summary statistic (e. g. mean or median) from a dataset. Their estimation is robust and does not depend on any assumed underlying model. If a model is assumed, the kernels can then be used to characterize specific components of the model. For example, the LN model is widely adopted for this type of application  [49] ; in its psychophysical variant, this model reduces to template matching for the L stage  [51] , i.
To this end, we applied the following energy-based recalibration to the stimulus samples in our dataset: each waveform as it was delivered to the listener was split into 9 segments, and RMS (root-mean-square) was computed from each segment to obtain a proxy AM vector equivalent to the 9-element vector specified by our amplitude modulation protocol. We then applied the same kernel estimation procedures used with the prespecified noise samples to these RMS-corrected samples. The resulting kernel estimates are plotted in S6 Fig, where it can be seen that they demonstrate the same characteristics as obtained before RMS recalibration.
Cascade approximation of auditory modulation filter models Data from reverse correlation experiments are almost invariably interpreted with relation to a cascade that only incorporates one linear filter L 1 applied via template-matching, followed by a threshold conversion to binary decision  [49]. However, as discussed in Materials and Methods, this linear-nonlinear (LN) class of models is inadequate for our dataset (we return to this point in Discussion). Instead, we adopt the more general and highly successful linear-nonlinear-linear (L 1 NL 2 ) cascade  [64]  which, in its most general formulation, serves as a functional approximator of wide applicability  [75, 76]  (notice that the L 1 linear stage in the L 1 NL 2 cascade involves convolution, not template matching; see Materials and Methods for further details).
As we demonstrate in later sections of this study, it is also an evolving characteristic that may not be present at all stages of stimulus exposure. We quantify this band-pass finding using four different metrics (Fig  4 ; see Materials and Methods). In all cases, L 1 estimate distributions (solid histograms in Fig 4B  -4D ) fall within the highpass/band-pass range, while L 2 estimate distributions (open histograms) fall within the low-pass range (the latter result points to a late temporal integration window of 50-100 ms consistent with independent estimates from previous studies  [21] ). More specifically, spectral centroids ( It is also noteworthy that results were comparable between datasets from increment detection decrement detection (y axis): there was no statistically significant difference (at p>0.
05) for any metric and for either L 1 or L 2 (data points scatter around solid unity lines in S3 Fig). Such convergence of independent datasets indirectly validates our estimation procedure and suggests that increments and decrements may be processed by the same perceptual mechanism (as also indicated by the similarity in performance metrics, see S1 Fig). The overall conclusion from the above analyses is that the L 1 filter is band-pass; therefore, AM processing resembles the characteristics of the MFB more than the LED model. This conclusion is not the product of fitting either model to the data: it is based on non-parametric characterization of the front-end filter associated with a general framework cascade that encompasses both models (Fig  2 ).
exhibited band-pass characteristics. In contrast, the 'early' estimate (green in Fig 5A ) was closer to low-pass (see also AM frequency plots within inset). Similar exposure-mediated changes in kernel structure have been previously reported in the vision literature  [31, 32] ; Although more elaborate interpretations are possible, a parsimonious view of our measurements suggests that the shift involved an adjustment of the same underlying filter population, rather than ad-hoc neural assembly of a new filter bank: the 'late' dataset overlaps with the 'early' dataset in Q-centroid space, only restricted to a smaller region (compare black and green ovals in Fig  5B ). When we compare the Q/centroid ranges spanned by the two epochs, we find that the lower percentile boundary (5%) shifts from 0.
Second, successful discrimination in our task was supported by both lowpass and bandpass stimulus power (see two peaks in S5I Fig) ; in this respect our protocol differs from the equally valid ones adopted by previous studies (e. g. [82] ) with the specific goal of excluding loudness cues (see Materials and Methods for clarifications as to why we deliberately avoided a stimulus design that would invalidate loudness cues). Because filter structure shifted between these two equivalent sources of task-related information (see above), discrimination performance may well remain unchanged even though supported by different regions of AM frequency. Prompted by the above results, we re-analyzed data from a prior published study  [58]  to determine whether similar effects could be exposed for an independent dataset collected using substantially different stimulus/task designs.
At the time when this dataset was published, the exposure-mediated effects reported in the present study were not known. We converted perceptual filters from the previous study into a format comparable with the one adopted here and This study represents the first targeted application of psychophysical reverse correlation to AM processing. Although related tools have been applied successfully in auditory neuroscience  [83]  and psychoacoustics (see  [84]  and  [85]  for the case of spectral processing and  [48]  for an application to a loudness illusion), they have not addressed the specific case of modulation perception  [86] , possibly due to multiple challenges associated with this question. First, there is the critical issue of which stimulus dimension should be perturbed by the noisy process in order to provide meaningful and feasible leverage for tapping into the mechanisms responsible for analyzing AM signals.
To appreciate the significance of the analysis adopted here, it is instructive to consider first-order filter estimates from target-absent noise modulations alone (orange trace in Fig  3A ). This measurement is similar to the decision weight profiles reported by loudness studies  [44] [45] [46] [47] [48] , including evidence for a primacy effect (larger weights during early phase of the stimulus  [47] , see orange trace in Fig 3A ), and is often regarded as a more appropriate description of the filtering process  [88, 89] ; more importantly, these measurements (from previous studies as well as our own) present lowpass characteristics, with no evidence of bandpass AM filtering. Signatures of bandpass processing are exposed specifically by second-order kernels (Fig  3B ). Indeed, when the dataset from  [48]  is re-analyzed using the nonlinear tools described here, bandpass filtering becomes evident at the level of second-order kernels in the presence of decisively lowpass first-order kernels  [90]  (see  [91, 92]  for related examples from the vision literature).
A further enabling factor in the experiments reported here is the stability of task rules and impenetrability of cognitive introspection. Specifically, listeners in these experiments carried out the same task throughout data collection and it is extremely unlikely that from one trial to the next they could explicitly monitor all small deviations introduced by the noisy process and introspect cognitively on those to reach a decision. They therefore operated under relatively stable conditions (except for potential intrinsic changes in adaptive state), allowing us to confidently treat our dataset as reflecting the properties of the same perceptual machinery throughout  [31, 32]  though specific parameters within that machinery may change with exposure). These advantages carry the cost of unusually large data mass (for this study we collected *100k trials), restricting our investigation to only a limited ecologically relevant portion (3-12 Hz) of the system's operating regime, however they allow us to exclude stimulus-driven alterations.
More specifically, previous literature has shown that auditory perceptual templates depend not only on signal spectrotemporal structure  [26]  but also on signal intensity  [27]  (see  [92] [93] [94]  for related results in the vision literature). Furthermore, strategy shifts not dissimilar from those we report here can be triggered by simple stimulus modifications such as signalmasker asynchrony  [28]. It is therefore critical to use stable task rules and a statistically invariant stimulus throughout (as we have done in this study) if one is to ascribe spontaneous filter changes to the perceptual system alone. Over the past decades, evidence in favour of LED/MFB models  [7, 20]  has been interpreted in the light of fitting procedures around specified computational implementations and often requiring ad-hoc adjustments for different datasets, weakening the associated conclusions regarding the applicability of one model over the other.
Our approach did not favour any specific model, nor did it involve explicit implementation of those or other computational schemes. At the same time, it enabled model selection while retaining close proximity with data structure. These methods uncovered a more complex picture than initially suggested by the LED/MFB dichotomy. Although the low-pass/band-pass distinction retains its descriptive power in relation to our dataset, the underlying mechanism displays dynamic adaptive properties that potentially depend on various factors, most notably learning-mediated plasticity  [95] [96] [97] [98] [99]  (see also expectation effects on AM processing  [29] ). This raises the possibility that previous diverse interpretations of the results (with respect to lowpass versus bandpass filtering) may in fact both provide adequate representations of the underlying process, albeit under different learning states  [18].