dataesr/scientific-paragraphs-categorization huggingface dataset:
Main Text Tool use and language are advanced human abilities which might have co-evolved  1. Their potential co-evolution predicts that tool use and linguistic processes may rely on common neural and cognitive resources  2. Recently, we revealed that tool use and syntax share cognitive processes and neural substrates within the basal ganglia (BG)  3. The fundamental question therefore opens of whether tool use also shares other crucial components with language. Tool use relies on a left fronto-parietal network, encompassing the inferior frontal gyrus (IFG) and inferior parietal lobule (IPL)  4, 5  , as well as subcortical structures, like the BG  6. This left fronto-parietal stream supports the planning and execution of tooluse movement  5  , while the BG may support the sequential and hierarchical aspects of tool use  3, 6.
For semantic processing, this issue has been in part addressed recently in studies involving the observation of various actions  13, 16. However, observing actions is not strictly parallel to their execution and all actions do not require the use of tools  19. Here we tested the hypothesis that activity in specific nodes of the tool-use network allows to decode word semantic categories or phonemes, through multivariate pattern analyses (i. e. representational similarity analyses -RSA) within the same participants. This would be proof that the neural signal in tool-use-related areas contributes to semantics and phonology.
Semantic but not phonological categories are decoded within the tool-use network To test our prediction that activity within the tool-use network contributes to semantic processing, we conducted representational similarity analyses in the left IFG, left IPL and left OTC, three crucial nodes of the tool-use network with a documented role in language, as described above. We reasoned that if tool-use-related regions contribute to semantics, primed and unprimed tool nouns should elicit similar neural representations in this network. The same rationale should apply for primed and unprimed animal nouns. By contrast, as a proof for a representation of semantic categories, tool and animal nouns should elicit dissimilar neural activities. This assumption was tested with RSA: the distance between the neural patterns elicited by each pair of semantic conditions (Tool primed, Tool unprimed, Animal primed and Animal unprimed) was estimated as 1the Pearson's correlation between the respective patterns.
In other words, the neural activity within the left IFG and left OTC clusters activated by tool use, allows to decode words semantic categories. Using an analogous approach, we then tested whether the left IFG and IPL clusters activated by tool use, could decode the phonological input based on the neural activity elicited by the speech perception task within those regions. The RDM consisted to test whether the phonological categorical perception phenomenon can be reflected within the left IFG and IPL (Fig. 2b  for the group mean). This RDM was defined for each subject individually based on their categorical boundary between /ba/ and /da/. Syllables perceived by the participants as belonging to the same phonemic category (i. e. /ba/ or /da/) should elicit more similar neural representations in the targeted regions than when pertaining to different categories.
This provides evidence that neural activity for semantics is supported by neural resources shared with tool use. This finding empirically supports the language embodiment theory  [22] [23] [24] [25] [26]  predicting that semantic representations are grounded within modality-specific systems for action and perception. More generally, it comes in support of the idea that semantic representations are supported by a multimodal representations built on previous sensorimotor experiences  27. In the motor domain, neuroimaging studies previously reported contribution of the left IFG and left OTC to tool-use behaviors  4, 5. More generally these cortical areas are part of the dorsal stream coding for goal-directed movements of the upper limbs  28, 29  and have been recently proposed to represent a gradient from abstract to more concrete motor representations  30.
Shared neural substrates might originate from the co-evolution of tool use and language  1. Experimental support for this assumption has been provided by previous studies  [33] [34] [35]. Our study however underlines that a better understanding of this functional link requires to capture the specificity of each linguistic function rather than studying language as a whole. Indeed, the co-evolution processes between tool use and language may not have affected each linguistic function equally. Accordingly, the neural circuits devoted to tool use might have been exapted  [36] [37] [38]  for specific language functions only. This specificity might depend on the degree of similarity between the computations performed by given neural structures (i. e. IFG and OTC for conceptual processing and BG for hierarchical processing  3  ).
Finally, the finding of common neuro-functional resources between semantics and tool use predicts potential reciprocal impact of one function over the other at the behavioral level  3, 39. Coherently, the use of novel tools that received a label, which may have sharpened their semantic representation, is facilitated in comparison to unlabeled tools  40. We also predict the so far untested reverse influence, namely that tool use may benefit semantic processing, in line with the reciprocal impact more generally documented between action and action-related language  41. Accordingly, shared neural resources between tool use and semantics may open new strategies for rehabilitation of brain-damaged patients  42. 001]). The response accuracy only revealed a trend for the effect of semantic category: participants tended to be less accurate for tool than for animal nouns (Tool nouns=96.
Participants The sample of participants, the motor task and fMRI acquisition parameters for this dataset have been described in a previous publication  3. fMRI acquisition included 24 (11 females and 13 males) right-handed, French native participants. All had normal or corrected-tonormal vision, and they reported no history of neurological, auditory, language or sensorimotor deficits. Four participants were excluded, two did not fulfill the familiarization performance requirements before any neuroimaging acquisition, one dropped out after the inclusion phase and one was removed from analyses due to substantial head movements (several runs with movements above 1. 5 mm). Hence, we analyzed the data of 20 participants with the following sociodemographic characteristics and manual preference: 10 females and 10 males; mean age ± SD: 24 ± 4 years old; higher education level: 3 ± 2 years; mean score on the Edinburgh handedness inventory  43  : 0.
80 database  44  (Table  S4 ). The two lists of animal and tool nouns were then each divided into 5 lists of 14 words each. The resulting 10 lists (5 animal nouns and 5 tool nouns) were matched for the aforementioned psycholinguistic variables (Table  S4 ). Each list was uniquely assigned to a function in the priming paradigm (either prime or target) so as to create five experimental conditions: Tool Primed, Tool Unprimed, Animal Primed, Animal Unprimed and Pseudowords. For words, primes and targets were associated by pairs in four conditions: a prime could be followed by a target either from the same semantic category (i. e. Tool Primed and Animal Primed) or from a different semantic category (i. e. Tool Unprimed and Animal Unprimed). In case of primed conditions, the words belonging to the same semantic category were always different.
Behavioral analyses For the semantic task, response times (RTs; i. e. time interval from the display of the target word to the participant's response) and response accuracy were measured to index semantic performance. Statistics on these data were run in R-studio with the afex package  45. A linear mixed model (LMM) was used on RTs and included Semantic Category (Tool nouns vs. Animal nouns) and Priming (Unprimed vs. Primed) as fixed-effect within-subject factors. These factors were also included as random slopes in addition to the random intercept for Subjects  46. Planned comparisons of RTs between semantically primed and unprimed conditions for each semantic category were performed with LMMs including Priming as fixed-effect within-subject factor and Subjects as random intercept. The Priming factor was also added as random slope only for the animal nouns.
Fundings CB is supported by  Swedish Research Council  grant ( 2015-01717 ) and  ANR-JC  ( ANR-16-CE28-0008-01 ). ACR by  ANR  ( ANR-16-CE28-0015 ). ACR and VB by  LabEx ASLAN  ( ANR-10-LABX-0081 ;  ANR-11-IDEX-0007 ).
Results Patient characteristics. A total of 944 participants were included: 830 patients with MGD and 114 control subjects. The mean age was 42. 6 ± 13. 2 years (range 18-87 years) in the MGD group and 40. 3 ± 14. 6 years (range 18-76 years) in the control group. The number of women in the MGD group and control group was 540 (65. 0%) and 84 (73. 7%), respectively. There was no significant difference in age (P = 0. 057), gender (P = 0. 068) and living area (urban or rural, P = 0. 188) between these two groups. The demographic data and basic medical history of MGD patients and normal controls are summarized in Table  1. The survey of systemic diseases showed that the MGD group had higher prevalence of hyperlipidemia (8. 8% vs. 0. 8%, P = 0. 003) and OSAHS (obstructive sleep apnea-hypopnea syndrome) (15.
Meibum expressibility and the quality of the central five glands in the middle third of the lower lid were evaluated at the slit lamp using the Meibomian Gland Evaluator (TearScience Inc. ). Meibum expressibility was scored as: 0 is all five glands expressible and 1 is four, 2 is three, 3 is two, and 4 is 0 glands expressible. Meibum quality was scored as 1, clear; 2, cloudy; 3, cloudy and particulate; and 4, inspissated, and was recorded as the highest grade expressed by the examined glands  38. The LLT, MGL were measured with the Lipiview II® device (Tear science, Morrisville, NC, USA). With the auto-analysis and calculation of the detection process from the Lipiview II® device, PB was also recorded. The loss ratio of the meibomian glands could be detected after inverted upper and lower lid with infrared imaging technology and ImageJ (National Institutes of Health) software  39.
Data analysis. Statistical analysis was performed with R software (www. r-proje ct. org). For each patient, the right eye was chosen for statistical analysis. Kolmogorov-Smirnov test was used for testing the normality of each variable. Mean values and standardized deviations were used to make the basic statistical description for normally distributed continuous variables, otherwise, median values and interquartile range (IQR) were used. Frequency and percentile were used to make a basic statistical description for categorical variables. An independent two-sample t-test was used to make the comparison of normally distributed continuous variables between the MGD group and control group, while the Wilcoxon rank-sum test was used to make the comparison for nonnormally distributed continuous variables.
Some open source software programs are also available. MouseMove is based on the commercially available LabVIEW development system and gives information like travel distance, speed, turning and curvature for mice during an open field behavioral test  (Samson, Ju et al. 2015). Another project based on commercially available Matlab language enables very precise behavioral phenotyping (e. g. drinking, eating, grooming, …) using a video database as reference  (Jhuang, Garrote et al. 2010). Some limitations are that the system needs high contrast (dark mice on a white background), recording from the side view, and process one cage at a time. Another project for studying neurodegenerative disorders presents a high precision motion analysis using 6 cameras, markers fixed on the animal, and a constrained walkway  (Karakostas, Hsiang et al.
Moreover, the available systems have not been developed and thoroughly tested for identifying seizures. Thus, there is a need for a simple software to acquire videos from a large range of cheap IP cameras, perform a quick and intuitive analysis on a standard computer, in particular for identifying seizures, and produce data in common file formats. This is why we developed a package of two software programs that enable video acquisition and motion analysis, VASD (Video Acquisition and Seizure Detection) and SASDI (Semi-Automatic Seizure Detection on Images), which we used to rapidly identify convulsive seizures, which are characterized by a sudden rise in motion amplitude and frequency power of the videos.
Algorithms The same module is used by VASD and SASDI to analyze video files, it is based on opencv methods: 1) each frame of the video file is converted to LAB color (L=Lightness, A=green/red, B=blue/yellow) and Lightness channel is filtered with a Contrast Limited Adaptive Histogram Equalization (clip limit 3, grid size (8, 8)) to correct noise and bad shading; 2) the difference of each pixel of consecutive frames is calculated, filtered with a median blurring (aperture size 3) to remove isolated pixels' noise and thresholded (binary above 25) to remove background noise; 3) the resulting frame is used to calculate the sum of all pixels' values within each ROI and normalized to the maximal value (number of pixels times 255). Users should try to use similar ROIs for each animal because the ratio between the size of the animal and the ROI surface influences the calculated motion.
Development of a Python based package Our goal is to avoid the need of costly specialized setups and invasiveness, thus we have chosen to use simple and cost-effective IP cameras or webcams and standard or slightly modified cages. We chose python language because it can run under an open source license, making it freely usable and distributable (https://www. python. org/psf/, accessed the 1/6/2021). It is largely used in the scientific community, and it is cross-platform (Linux, Windows, Mac, etc. ). The video acquisition exploit FFmpeg (developed under GNU General Public License, https://ffmpeg. org/, accessed the 1/6/2021), the leading multimedia framework, which is cross-platform and deals with most of the available codecs, enabling to capture and save most video formats. The image analysis is based on another widely used open source package, opencv (https://opencv.
org/, accessed the 1/6/2021), enabling most image transformations and analyses. Finally, these scripts used packages from Python 3. 9 standard libraries and from open source python packages like Schedule (https://pypi. org/project/schedule/, accessed the 1/6/2021), Numpy (https://numpy. org), Matplotlib (https://matplotlib. org, accessed the 1/6/2021), and SciPy (https://scipy. org, accessed the 1/6/2021). Different approaches are possible for motion tracking  (Abbas and Masip Rodo 2019). We chose temporal differencing, which computes the pixel-by-pixel difference in consecutive video frames. This implies a non-stationary reference image, which is an important feature for analyses of long term recordings, because the animal surrounding changes with time (bedding, food, etc. ) and the light switches from white to infrared during day/night cycles.
Automated video acquisition and motion detection VASD (Video Acquisition and Seizure Detection) is designed to run on a linux server and enables to acquire videos from up to 8 IP cameras using a rtsp stream (exchange format that most IP cameras can provide). VASD acquires series of 1 hour-long video files stored in the mp4 multimedia container format generated using the highly efficient H265 codec. The acquisition duration can be very long, up to months (the only limitation is the hard drive storing space). The acquired video files correspond to camera full field or half fields (left or right parts of the field in separated video files). VASD performs automatically a motion analysis on each file, storing results in a csv format file. VASD analyzes the full acquired video field or both sides separately.
VASD uses multiprocessing module that enables the analysis of many videos in parallel, but only one thread is used for each video file because our test demonstrated that using more than one thread decreases the performance. For example, on a Dell server, CPU Intel Xeon Gold 5220, one video (15 frames per seconds, 640x480 pixels) is analyzed at a rate of 61 images/s (Hz) using 4 threads for the same video file, 119 Hz using 2 threads, and 123 Hz using 1 thread. At present, VASD does not work on Windows platforms because the used schedule package is not working properly (does not start at the proper time), but future versions of the package will probably allow to use it on Windows based computers. The motion analysis implemented in VASD is a normalized sum of variations in intensities pixel by pixel (as described in the above Algorithms paragraph).
This is performed for each Region of Interest (ROI). Results (motion index and time from video start) are saved to standard csv files. The VASD user interface is shown in Figure  3. 3. 1. 2 Automated motion detection and semi-automated seizure detection SASDI (Semi-Automated Seizure Detection on Images) can run on window or linux machines (not tested on iOS). It enables to select lists of video files (any format accepted by opencv module) from any source (previously described VASD or other video acquisition systems) and with any length (tested up to 72 h long videos, but depending on file format 1h long videos are more efficiently analyzed). SASDI allows the selection of a full directory or a set of video files. It also proposes a very powerful selection mode: the serie. A serie is a folder containing other subfolders with video files (no theoretical limits in number; this structure is the one used by VASD for its output videos).
To perform motion analysis, SASDI automatically used 80% of available threads but this value can be easily changed, in order to use all CPU threads, for maximizing the speed of analysis, or to use less threads, for being able to work at the same time with other software programs or not to overload a shared server. The parallel analysis of a multiple videos is very rapid (using one thread for each video in independent processes). For example, the analysis of 522 videos, recorded at 22 frames per seconds, on a server (Dell, CPU Intel Xeon Gold 5220) using only 10 threads and analyzing 2 ROIs of 640x720 pixels per video, was performed at 1090 Hz. The SASDI user interfaces for files selection and ROIs drawing are shown in Figure  4. SASDI is also the reader for motion analysis files created by either VASD or SASDI itself (motion index against time, in csv format) and their corresponding video files.
VASD and SASDI is a free package that only needs cost-effective webcams and a computer to run. It allows the acquisition and analysis of hundreds of hours of video recording, providing high efficient identification of convulsive seizures and an overall index of the animal activity. This noninvasive approach allows the study of animals without any surgery and electrode implantation, and also allows to perform chronic studies of young animals, which are particularly difficult with implanted electrodes. In fact, we successfully used it for litters of pups aged 10 to 30 days (with their mother up to age 21 days); it is still a major gain of time compared to visual inspection of the full video recording, even if there are much more false-positive than with individual animals. The main limitation of our image analysis system is that it cannot detect non convulsive seizures, because they are very close to normal activity.
Acknowledgements We gratefully thank Damien Barbier for the linux server settings. This research was supported in part by University Cote d'Azur "Académie d'Excellence 4" (https://univ-cotedazur. fr/structures-derecherche/academies-dexcellence/academie-4/projets/projets-finances, Epi-Analyse), University Cote d'Azur UCA-JEDI (https://univ-cotedazur. fr/ucajedi-lidex-duniversite-cote-dazur, ANR-15-IDEX-01) and by the Laboratory of Excellence "Ion Channel Science and Therapeutics" -LabEx ICST (https://www. labex-icst. fr/en, ANR-11-LABX-0015-01).
However, it is still unclear if and how those associations with neurobiological indices change over time, e. g. , during adolescence  (33) , and if those brain activity patterns have a predictive value for later changes in self-report and/or neurocognitive measures of impulsivity  (35). Our study addressed these gaps by capturing brain activity data during inhibition error processing and reward anticipation and relating them to self-report measures of trait impulsivity and neurocognitive indices of decisional impulsivity. Associations were explored longitudinally during adolescence until young adulthood in a large population-based sample using cross-correlations, mixed-effect models, and predictive modeling.
Participants Anonymized data for the present study were obtained from the IMAGEN project  (35) , a multicenter study including 8 sites (London, Nottingham, Dublin, Mannheim, Dresden, Berlin, Hamburg, and Paris). Healthy participants were longitudinally assessed at baseline during adolescence (at around age 14) and reassessed at follow-up in young adulthood (at around age 19). Ethics approval was provided by the local ethical committees for each participating center, and written informed consent was obtained from parents or guardians. Furthermore, verbal assent was obtained from the adolescents. As young adults, participants provided written informed consent. Exclusion criteria were serious medical conditions, previous trauma with loss of consciousness, any magnetic resonance imaging (MRI) contraindications, or IQ ,70.
Details regarding the study protocol and data acquisition have been published previously  (9). Participants were included in the current analyses if they had at least one facet of either trait impulsivity or decisional impulsivity assessed via self-report questionnaires or neurocognitive testing, respectively, and one functional MRI (fMRI) task of interest at baseline. Finally, data from 2034 healthy adolescents (mean [SD] age = 13. 96 [0. 45] years; IQ = 108. 15 [13. 53] ; 49. 4% male) and 1383 of these participants as young adults (age = 19. 09 [0. 77] years) were included.
Data Acquisition Imaging data were acquired at each of the 8 sites with 3T MRI scanners by different manufacturers (Siemens, Philips Healthcare, GE Healthcare, Bruker). Full details of the MRI acquisition protocols and quality checks have been published previously  (9). fMRI images were acquired using an echoplanar imaging sequence. For each subject, 444 volumes were acquired for the SST, and 300 volumes were acquired for the MID task. For both tasks, each volume consisted of 40 slices (2. 4-mm slice thickness, 1-mm gap), and to provide reliable imaging of subcortical areas, echo time was optimized (echo time = 30 ms, repetition time = 2. 2 s). The same scanning protocol was used at all sites.
Data Preparation Data were z-standardized, and confirmatory factor analyses  (44)  were conducted on the indices for trait impulsivity (NEO Five-Factor Inventory, Substance Use Risk Profile Scale, Temperament and Character Inventory-Revised) and delay discounting K1-K3 values for the Kirby delay discounting task, respectively, to form one latent factor using the R software package lavaan, version 0. 6-6 (http://cran. uni-muenster. de/ web/packages/lavaan/lavaan. pdf). Predicted values were calculated for each participant estimating the factor scores on each construct. As only one value from the CGT delay aversion subscale was used, no factor analysis was conducted for this impulsivity index. fMRI data were analyzed with SPM8 (https://www. fil. ion. ucl. ac. uk/spm/) and MATLAB, version 2011b (The MathWorks, Inc.