dataesr/scientific-paragraphs-categorization huggingface dataset:
similar or had a similar spoon at a younger age), the hand preference for a spoon according to the parents (left, right, and nonlateralized), the presence of a rake-toy at home (yes/no), and the previous observations by the parents of their child using a stick or a similar tool to retrieve an out-of-reach toy at home (yes/no). Exploratory analyses were performed in order to identify potential differences between infants among these variables according to their performances with the rake and/or the spoon. The data extracted from the questionnaire were analyzed using the FAMD (package FactoMineR). This methodology is known to be appropriate when both quantitative and qualitative variables (i. e. , mixed data) are analyzed simultaneously, and when the relatively limited number of individuals (usually less than 100 individuals) prevents running a multiple correspondence analysis by transforming the quantitative variables into qualitative variables.
With FAMD, infants are represented in a multidimensional space based on the response to each question from the questionnaire. Individuals with similar profiles will lie together in the same part of the space, and individuals with reversed patterns will lie on opposite sides of the origin. This analysis allows us to explore if some profiles of previous experience with spoons and tools could be related to the performance with the spoon and the rake. Before running the FAMD analysis, we used the complementary package missFDA to extrapolate missing data, in the rare cases where some answers were missing in the questionnaires because the parents lacked the answer to a question (4. 1% of the overall data). One infant who was successful at the spoon-toy condition of the facilitation phase had to be removed from this analysis because of a change in the procedure (the infant lost interest in the toy and the reward was replaced by a piece of biscuit).
Acknowledgment Author Contributions: Jeancolas and  Rat-Fischer  contributed equally to this work. We thank  Viviane Huet  for managing the list of families to be contacted for our studies. We thank  Lucie Martin  for her assistance during testing. We are grateful to the parents and infants who participated in the study. This work was funded by the  Foundation de France ,  ERC Advanced  grant  323674  "FEEL" and  CNRS.
As the quantitative analysis of academic literature, bibliometric analysis is commonly utilized on a given topic to evaluate the quantity of publishing literature, their academic impacts, research fields, or keywords  [15]. It can efficiently elucidate the landscape of the existing publications, and it may also shed a light on the area requiring more effort. Herein, we conducted a bibliometric analysis of the published literature on axon regeneration based on the database named Web of Science (WoS) over the past 22 years and applied an unsupervised machine learning named latent Dirichlet allocation (LDA) analysis to screen the sheer volume of literature. LDA is a generative probabilistic model to collect documents which has been widely used to uncover latent topics on a large scale  [16, 17].
Data collection and general data analysis To obtain eligible studies published from 2000 to 2021, our study conducted a literature search in the core collection of WoS, including Science Citation Index Expanded, Social Science Citation Index, Conference Proceeding Citation Index-Science, Conference Proceeding Citation index-Social Science and Humanities, Index Chemicus, and Current Chemical Reactions. The keywords used in search strategy were as follows: "axons, " "axon, " "axonal, " "regeneration, " "regrowth, " "reinnervation, " "repairment, " "repair, " and "recovery. " The general information of the search outcomes was extracted as an XLS file, including the titles, authors, affiliations, abstracts, publication years, total citations, journals, countries/regions, funding agencies, and research fields.
Framework of the analysis To illustrate the framework of the analysis procedure, the implementation steps are depictured in Fig. 1. The analysis is classified into three parts. First, the data collection is implemented based on the WoS to provide the database for the subsequent analyses. Second, general data analysis is conducted using Excel to investigate the distribution of publications, leading journals, leading countries/regions, funding agencies, and leading research fields. The LDA analysis is performed to identify different research topics and hotspots to obtain their relationships. Third, the visualizations of the results are implemented using Gephi version 0. 9. 2 and Origin version 9. 1.
Leading journals Based on data analysis, the articles related to axonal regeneration from 2000 to 2021 were published in 2206 different journals. A third of these documents were published by the leading 20 journals listed in Table  2. The most prolific journal is Experimental Neurology with 826 publications in total, while the Journal of Neuroscience has the highest H-index. To identify the core journals in this research field, we further analyzed these journals by the combination of their impact factor (IF) and the average number of citations per document (ACD). As shown in Fig. 3 , Brain is the journal possessing the largest ACD and IF simultaneously, followed by the Proceedings of the National Academy of Sciences of the United States of America and Biomaterials. As for the most influential journals, Cell, Nature, and Science published 13, 28, and 38 high-quality documents during this period, respectively.
Limitation Admittedly, there were some limitations of the current study. The literature search was only on the basis of the WOS collection indexed journals, which is a common limitation of similar studies  [72, 73]. Future bibliometric studies could be conducted on other medical science databases, which would offer a supplement to more comprehensively understand this field. Despite the LDA analysis can extract not only the topic of articles more precisely and easily but also the connections between different topics can be analyzed, the LDA theme was created by artificial intelligence, and these themes were on the basis of machine-driven understanding. Moreover, small specific niches were not spotted by this analysis. Indeed, in this study, we did not highlight key studies in the field of CNS repair such as the work from the Courtine lab; they combine pharmacological approach to engineering to activate dormant circuits in order to allow functional recovery  [74] [75] [76].
This study aimed to characterise typical patterns of infarct, plus structural and functional disconnection, in a sample of 23 SA patients. The use of such 'connectomic' data may help to elucidate the relationship between diffuse networks and impairments following cerebral insult  (Fornito et al. 2015). We assessed the impact of lesion on functional networks including the SCN, MDN, and DMN, given all these networks are implicated in semantic cognition. Individual patterns of structural disconnection were predicted by tracking white matter fibres likely to pass through a patient's lesion based on diffusion-weighted imaging data from neurologically healthy participants  (Foulon et al. 2018). Structural disconnection in stroke patients assessed in this way has been shown to reflect functional activation better than lesion location alone (Thiebaut de  Schotten et al.
Principal components analysis (PCA) with oblique rotation was used to extract a composite score for semantic tasks that were maximally available across the sample: namely word and picture CCT, and overall performance on the no cue version of the 'ambiguity' assessment  (Noonan et al. 2010). In doing so, we aimed to obtain a single score to reflect semantic performance across tasks for each participant. PCA was performed with data from the 21 patients who completed all three tests. This analysis revealed that all three tests loaded strongly on a single component with an eigenvalue of 2. 6 (loadings: CCT words = 0. 95; CCT pictures = 0. 92; ambiguity = 0. 92) that explained 87% of variance in performance. We extracted a 'semantic cognition composite score' from this component, with lower values reflecting greater impairment of semantic cognition.
Data were collected from the whole brain on a 3 T GE HDx Excite MRI scanner using single-shot 2D gradient-echo-planar imaging (TR = 3 s, TE = minimum full, flip-angle = 90°, matrix size = 64 × 64, 60 slices, voxel size = 3 × 3 × 3 mm, 180 volumes). We excluded sixteen participants: nine due to missing behavioural data, one due to missing MRI data, one due to incorrect TR in MRI acquisition, and four during pre-processing because they exceeded our motion cut-off of 0. 3 mm, had more than 20% invalid scans and/or mean global signal change of z > 2. The final sample size, therefore, consisted of 191 participants. Estimates of structural disconnection were based on diffusionweighted data from healthy controls, collected on a 3T GE Signa HDx TwinSpeed system (TR = 20/30 R-R intervals, TE = 93. 4 ms, matrix size = 128 × 128, 60 slices, voxel size = 2.
Structural disconnection The BCBtoolkit  (Foulon et al. 2018 ; http:// www. toolk it. bcblab. com) was used to generate probabilistic maps of spreading structural disconnection. This approach uses a set of ten healthy controls' DWI datasets  (Rojkova et al. 2016)  to track fibers passing through each patient's lesion. This makes it possible to estimate likely structural disconnection from a lesion even when no diffusion-weighted imaging is acquired. The control sample used here is independent of the sample used to estimate functional disconnection (see Section "MRI acquisition"). Patients' lesions in the MNI152 are used as seeds for tractography in Trackvis  (Wang et al. 2007). Tractographies from the lesions were transformed in visitation maps (Thiebaut de Schotten et al. 2011) and binarised.
2021). All PCAs and functional connectivity analyses were conducted using the CONN functional connectivity toolbox V. 20. b (Whitfield-Gabrieli and Nieto-Castanon 2012; www. nitrc. org/ proje cts/ conn) in MATLAB. Functional resting-state volumes were skull-stripped, slice-time (interleaved) and motion-corrected, and coregistered to the high-resolution structural image, spatially normalised to MNI space using the unified-segmentation algorithm, smoothed with a 6 mm FWHM Gaussian kernel, and band-passed filtered (0. 008-0. 09 Hz) to reduce lowfrequency drift and noise effects. A pre-processing pipeline of nuisance regression included motion (twelve parameters: the six translation and rotation parameters and their temporal derivatives) and scrubbing (all outlier volumes were identified through the artifact detection algorithm included in CONN with conservative settings: scans for each participant were flagged as outliers based on scan-by-scan change in global signal above z > 3, subject motion threshold above 5 mm, differential motion and composite motion exceeding 95% percentile in the normative sample).
Acknowledgements We thank  Zhiyao Gao  for his assistance in the procurement and interpretation of functional network maps, and  Lucy Cogdell-Brooke  for her guidance in manual lesion tracing. EJ was funded by a  European Research Council   Consolidator grant  ( FLEX-SEM -771863 ). MTS was also funded by a  European Research Council   Consolidator grant  ( DISCONNECTOME, Grant  agreement No. 818521 ). AH was funded by the  Rosetrees Trust  ( A1699 ) and  European Research Council  (GAP:  670428 -BRAIN2MIND_NEUROCOMP  to MLR).
19 e selected from the database participants with a positive genetic test. The SPATAX cohort was coordinated at the Pitié-Salpêtrière University Hospital in France and hosted at the Paris Brain Institute. Participants were eligible when they had a pathological CAG expansion in ATXN1, 2, 3 and CACNA1A and at least one available SARA score. The inclusion started in 2005 (first visit with SARA assessed) until 2020, and the average baselineto follow-up interval was 1 year. 18.
As the FDA did not accept m-SARA, the new f-SARA was created with input from the FDA and used as an outcome in Biohaven troriluzole trials. 15 Like m-SARA, f-SARA consists of items 1 to 4, each with a score range of 0 to 4, but with minor changes in the score categories. f-SARA is supposed to be applied prospectively. Nevertheless, there remains a close relationship between SARA and f-SARA. To compare SARA and f-SARA in existing data sets possible, we defined a mapping algorithm (Fig. S1 ) that allows transformation of SARA into f-SARA data. It must be noted that transformation of SARA data into f-SARA is only an approximation, because f-SARA scores were not obtained prospectively. In our study, we compared the temporal dynamics of the f-SARA scale, obtained with the transformation of SARA, with the original SARA scale (Table  1 ).
We used the opensource software Leaspy, ordinal version, to estimate the model parameters from a longitudinal data set. 15 he typical progression of the disease was characterized by the population parameters delta, corresponding to the average time (in years) spent at each level of the score. For instance, delta 1 is the average time spent at SARA score 1. The temporal dynamics of each participant was characterized by two individual parameters, one for the speed of progression (acceleration factor, Xi) and the other for the start of progression (time shift, Tau). The start of progression characterized the age in years at which the SARA score changed from zero to one, whereas Xi is the multiplicative factor compared to the population progression (Appendix S1).
Data Sharing Anonymized data can be obtained from the coordinators of the four source studies on reasonable request. participants (P < 0. 001 and P < 0. 001, respectively). These SCA1 participants had lower SARA scores at baseline than SCA2 participants (10. 5 [4. 2; 17] vs. 13. 0 [8;  18], P = 0. 014) and SCA6 participants (13. 0 [8. 5; 18. 5], P = 0. 029). As expected, SCA6 participants were older at baseline with an older age at onset. At baseline, the maximum score was reached for 6. 67% of patients for items gait and stance, 3. 34% for sitting, 0. 79% for speech, 1. 67% for finger-chase, 1. 35% for finger-nose, 1. 99% for hand fast, and 3. 57% for heel-shin.
Notice that in this work we are dealing with many parameters that specify threshold voltages; we use V TH for the threshold of the LIF behavior (figure  1 ), V BF for the voltage threshold of the SCRs, and, V TH1 K V TH6 for the threshold of the MOSFETs. For the sake of clarity, in most discussions below we shall neglect second-order or parasitic effects such as, for example, gate leak in MOS transistors (nevertheless they are considered in all the numerical results). For the simulations presented in sections 2-4, we use the software LTspice  [11]. The short channel models for the VLSI transistor are reported in  [12]. A basic implementation of the LIF model is presented in figure 1(c). We assume an input current that in general is variable in time. This current is integrated by the capacitor C, while the leaky resistor R slowly discharges it.
Acknowledgments This work was partially conducted within the framwork of the  AIST-CNRS-CentraleSupélec Joint Research Agreement 'Bioinspired electronic  systems'. MJR acknowledges support from the  French ANR  project  'MoMA '  ANR-19-CE30-0020. Author contributions: PS, OS, and MJR designed the architecture of the circuit in figure 4, obtained the results presented in section 4, and wrote the manuscript (except section 5). PS, IA and MH adjusted the circuit to VLSI standars. IA did the CMOS implementation in TSMC 65 nm CMOS process and obtained the results presented in section 5 and wrote that section. All the authors contributed to the final revision of the manuscript and discussion of the results.
METHODS Briefly, blood and urine samples of patients with acute brain ischemia were collected within 4. 5 h of symptoms onset (day 0, D0) and then at 24 h (D1) and M3 from symptoms onset. The blood and urine samples of controls were collected once (D0). TSK axis markers were measured and compared between patients and controls and between different time points, as detailed in the following paragraphs. Detailed inclusion and exclusion criteria, clinical evaluation and demographic data of the participants, and a flowchart of the study design are provided in the Supplementary Material.
Statistical Analyses Non-identifying data from clinical and biological measurements were analyzed using Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS) software. The Shapiro-Wilk normality test was used to evaluate the data distribution. Student's T-test was used for comparisons between patients and controls for normally distributed data and Pearson's correlation coefficient was used to evaluate relationships between variables. Qualitative data were compared using Fisher's exact test and quantitative non-normally distributed data using Wilcoxon's non-parametric test for paired samples when appropriate. Spearman's non-parametric correlation coefficient was used to evaluate relationships between non-normally distributed variables. All tests were two-sided and a p < 0.
B cells (blue points) are connected to A cells (red points) via excitatory synapses (pink arrows, denoted W B A ) and to RG cells (green points) via excitatory synapses (brown arrows, denoted W B G ). A cells are connected to B cells via inhibitory synapses (green arrows, denoted W A B ) and to RG cells via inhibitory synapses (cyan arrows, denoted W A G ). The voltage of RG cells is sent through a non linearity (pink curve in the black circle) so as to produce spike trains conveyed to the LGN. physically detailed models  [26, 27, 28] , Generalized Linear Models applied to retinal coding  [29, 30, 31]. Several powerful software has been designed to model the retina at different scales such as COREM  [32] , Convis  [33] , Isetbio https: //github. com/isetbio/isetbio/wiki. The Virtual Retina simulator, developed by A.
Wohrer and P. Kornprobst  [34]  at INRIA was one of the first of these simulators and has given rise to subsequent simulators in our group, the platform PRANAS  [35] , https://team. inria. fr/biovision/pranas-software/ and more recently Macular https://team. inria. fr/biovision/macular-software/. There are quite less mathematical results on how retinal structure, especially lateral A cells connectivity, shapes the spike response to spatio-temporal stimuli  [36, 37, 38]. One of the goals of this paper is to elicit reflections in this direction, grounded on mathematical developments fed by the recent progress in the knowledge of retina physiology and structure. This is a humble and partial point of view, resulting from my collaboration with neurobiologists experts in the retina. The paper contains new results, essentially the mathematically tractable model of the layered retina integrating amacrine cells lateral connectivity and the mathematical framework to handle piecewise linear rectification presented in section 2, the study of rectification effects on retinal ganglion cells receptive field (section 3.
Acknowledgements I would like to warmly acknowledge all the neuroscientist collaborators from which I learned about this beautiful object, the retina, and, more generally, about vision:  Frédéric Chavane ,  Gerrit Hilgen ,  Olivier Marre ,  Adrian Palacios ,  Serge Picaud  and  Evelyne Sernagor. I thanks the reviewers for their detailed review and helpful criticism which helped to significantly improve the paper.
These consisted of birds chirping, a dog barking, a fly buzzing, a frog croaking, and a rooster crowing. An independent ratings experiment confirmed these sounds were matched to the hand-object interactions for familiarity. Sounds from these two categories were downloaded from SoundSnap. com, YouTube. com, and a sound database taken from  Giordano, McDonnell, and McAdams (2010). The second control category were nonmeaningful sounds, defined as pure tones. These consisted of pure tones of five different frequencies (400Hz, 800Hz, 1600Hz, 3200Hz, and 6400Hz), created in MatLab. All sounds were stored in WAV format, and were cut to exactly 2000ms using Audacity 2. 1. 2, with sound filling the entire duration. Finally, all sounds were normalised to the root mean square (RMS) level  (Giordano et al.
Literature Review We performed a systematic review of the CPGs on the ASD diagnostic process published in English and French according to the definition of CPGs given by the Institute of Medicine: "statements that include recommendations intended to optimize patient care that are informed by a systematic review of evidence and an assessment of the benefits and harms of alternative care options"  [22]. For the literature review, the Guideline International Network  [23] , MEDLINE and HTA databases (INAHTA), and web search engines (Google and Google Scholar) were searched for relevant literature published between January 2005 and December 2019. We extended the date range of the search to 2021 for CPGs relevant to the selection criteria. For these searches, the following keywords, MeSH terms, and text words were combined: "ASD" OR "autism", OR "pervasive developmental disorder" AND "diagnosis" OR "diagnostic" OR "assessment" OR "evaluation".
Phoenixin In 2013, with the use of modern bioinformatics tools, advanced algorithms, and databases, such as the Human Genome Project, the Samson group identified a hitherto unknown peptide, which was called phoenixin. Successively, it was possible to confirm its presence in many species, including humans, rodents, pigs, cows, or zebrafish  [268]. Phoenixin is produced mainly in the hypothalamus by the proteolytic cleavage of a small integral membrane protein 20 (SMIM20). In humans, a gene for this precursor is located on chromosome 4 at position p15. 2  [269]. The hormone exists in many isoforms which differ in the length of aa sequences and are named phoenixin-42, -36, -26, -20, -17 and two predominant forms-phoenixin-20 and -14, which are the most active, and occur in larger amounts in many tissues.
Results This study aimed at investigating the large-scale hemodynamics during REMS, in particular deep structures that are not easily accessible by state-of-the art techniques. Using an experimental approach developed previously  35  , which included a cranial window, the implantation of local field potential (LFP) electrodes and a permanent fUS-compatible plastic prosthetic skull, and the mounting of the ultrasound probe holder (Fig. 1a ). In this setup, the different regions of the brain were monitored in a series of coronal and sagittal planes, each acquisition lasting 30 min for 4-6 h per day over the course of several days (Fig. 1a ). This resulted in a dataset of 72 recordings in n = 3 animals, totalizing 577 REM episodes recorded in 259 brain regions, together with hippocampal LFP recordings, accelerometer, and neck electromyogram (EMG) (see Supplementary Table  1  and Supplementary Figs.
1  and 2  for data acquisition and Supplementary Fig. 3  for electrode implantation and identification). Volumetric Reconstruction of REMS vascular networks. We performed measurements of the cerebral blood volume (CBV) in n = 3 rats, in a total of 72 recordings, during which animals are spontaneously going through different vigilance states (quiet wake -QW, active wake-AW, non-REM sleep-NREMS, REM sleep-REMS, see Methods for details regarding the classification). In order to render the mean activations levels for all brain structures and states, we performed a series of processing steps that included: (1) data acquisition along coronal and sagittal brain sections, (2) manual registration onto an anatomical atlas using salient landmarks, (3) ROI extraction and sleep scoring to finally compute regional averages of CBV time-series for all vigilance states and finally to re-project the mean values onto a 3D volume (Fig.