dataesr/scientific-paragraphs-categorization huggingface dataset:
Illumina sequencing, reads assembly and functional annotation To identify the OBP and CSP genes from B. tabaci MED, the cDNA from male and female heads were sequenced using the Illumina HiSeq 2500 platform. A total of 59,248,856 and 57,436,708 raw reads were obtained from male and female heads, respectively. After filtering out adaptor sequences, low quality sequences, 56,755,596 and 54,923,666 clean reads were generated from the heads of male and female raw data, respectively. Once assembled, 106,893 transcripts were obtained with an N50 of 2217 bp (Table  1 ). The raw reads of the B. tabaci MED were submitted to the GenBank Short Read Archive (SRA), under the accession number of SRX2403438 (female) and SRX2403439 (male). All the transcripts of the assembled transcriptome were blasted against nr datatbase (NCBI) using BLASTx and 23,572 transcripts were annotated, with an E-value below 10 -5.
Group data shows increase in firing produced by PSEM308 (3μM). (F) Extracellular recordings from LC neurons in anaesthetised rats using multi-barrel recording electrodes allowing local pressure ejection of PSEM308/saline/pontamine sky blue (PSB). Traces show graded excitation of an identified LC neuron by PSEM308. Recording sites were subsequently histologically identified by the PSB staining within the LC (transduced cells identified by IHC for EGFP (black arrowheads) and DBH (white arrowheads). The response to local PSEM308 was categorised as excitation or inhibition if it changed firing rate by more than 3 SD from the baseline rate. Application of PSEM308 produced an excitation in 50% of LC neurons. We found a second group of neurons that showed no response, presumably as they were not transduced.
In vivo extracellular recordings from the locus coeruleus and local application of PSEM308 Anaesthesia was induced with 1. 2-2 g/kg urethane (Sigma) i. p to achieve loss of paw withdrawal reflex. Once induced, urethane anaesthesia typically remained stable for 5-7 hours. The animal was placed in a stereotaxic frame, the skull exposed and the screw above the viral injection site removed to gain access to the CNS. In vivo single units were recorded with preloaded quadruple-barrelled microelectrodes (Kation Scientific, Carbostar-4 recording/iontophoresis microelectrode, E1041 standard) connected to a drug injector (Parker, Picospritzer II). Recordings were referenced against a silver chloride electrode pellet that was inserted between muscles and skin near the recording site. Neuronal activity was recorded via a Multiclamp 700A amplifier (with #CV-7b headstage, Axon instruments) filtered at 3 kHz and digitized at 10 kHz using a 1401micro and Spike2 software (CED, Cambridge Electronic Design) with custom scripts was used to store data and to control the drug delivery.
Warmed (36˚C), Krebs solution was used to continually superfuse the spinal cord (NaCl 117 mM, KCl 3. 6 mM, NaHCO 3 25 mM, NaH 2 PO 4 1. 2 mM, MgCl 2 1. 2 mM, CaCl 2 2. 5 mM and D-glucose 11 mM saturated with 95%O 2 /5%CO 2 , pH7. 3, osmolality 290mOsm/L). The dura mater was removed under binocular vision (Leica MZ6). A reference electrode (World Precision Instruments, Ag/AgCl Electrode pellets, EP2) was inserted into the muscle layer near the recording area and spinal units were recorded with a stainless-steel microelectrode (FHC, UESSEGSEFNNM, 8-8. 8MΩ) that was positioned ~500 µm from the midline. Neuronal activity was amplified and low pass filtered at 3 kHz using a Multiclamp 700A amplifier (Axon instruments with #CV-7b headstage). The data were digitized at 10 kHz (CED Power1401) and stored using Spike2 software (CED, Cambridge Electronic Design) with customized scripts to monitor pinch force applied from custom calibrated forceps with tip pads of area 3.
Immunofluorescence Tissue sections were blocked for 45minutes in phosphate buffer containing 0. 3% Triton X-100 (Sigma) and 5% normal goat serum (Sigma). Incubated on a shaking platform with primary antibodies to detect EGFP (1:3000),  DBH (1:2000) , or HA tag (1:500-1:1000) for 14-18 h at room temperature. After washing, sections were then incubated for 3 h with appropriate Alexa Fluor® secondary antibodies. A Leica DMI6000 inverted epifluorescence microscope equipped with Leica DFC365FX monochrome digital camera and Leica LAS-X acquisition software was used for widefield microscopy. For confocal and tile scan confocal imaging, a Leica SP5-II confocal laser-scanning microscope with multi-position scanning stage (Märzhäuser, Germany) was utilized. Images were exported as TIF files before being processed, analysed and prepared for presentation using Fiji-ImageJ software.
Study Area This study was conducted during 22 days in August and September 2013. The data were gathered in eight different urban parks located in the city of Antwerp (Figure  1 ). Antwerp has a population of about 500,000 people and an economy centered around an important European harbor. Therefore, the traffic going to and from the city has a big impact on the urban sound environment. The investigated parks, chosen in cooperation with the city council environmental authority, were expected to thoroughly represent the Antwerp park environment. They were located in different urban areas: city center (Stadspark), near the big highway junction (Rivierenhof) and in various living districts (six other parks). Moreover, their sizes also varied considerably from the smallest park (Bischoppenhof, around 0.
Mobile Measurements Measurements were performed using mobile recording devices carried in backpacks by two to three people per park. Researchers carrying the backpacks were specifically instructed to mind their walking manner in order not to disturb the recorded sonic environment. Paths were chosen to cover the whole area of the park while no specific walking directions were given. Additionally, approximately every half hour, the researcher would stop to make stationary recordings by placing the backpack on the closest bench for 10 min. Moreover, in order to capture the sound environment outside the park, the researchers also walked on the roads surrounding the park. The devices used in measurements were custom-made, Linux-based sensor network mobile nodes  [28]  adapted to incorporate both sound and location recording.
Therefore, the collected data comprised sound recordings, 1/3-octave band levels saved eight times per second, as well as GPS positions recorded one time per second. To facilitate data processing and presentation, spectral levels and GPS values were later transferred to a spatial database. A set of indicators characterizing the sonic environment was extracted from the stored measurement data. These indicators were calculated from recorded 1/3-octave band values selected with a moving window of one minute duration and 10 s time step. Thus, every 10 s, one value was calculated, integrated over the last minute. Consequently, there was a 50 s overlap of data with the previous indicator value. Finally, location data (GPS positions) were included and related to the acoustic indicators by interpolating the dataset to the same 10-second period division.
Prototypical group markings (g ki ) are provided for each statement i and three tranquility groups k: (1)-"Social relationships" group, (2)-"Natural sound sources" group, (3)-"Silence" group. Secondly, the set of persons j that answered value l to question Q (e. g. , "not at all" for question "hearing human sounds") was selected. Finally, the relative membership H Q,l (Equation (  4 )) was calculated as an average of the normalized degree of similarity G j (k) over the persons that selected the l-th answer to question Q (the sum in denominator in Equation (  4 ) effectively represents this number of participants). Additionally, for the complete representation of the selected dataset, standard deviations were also calculated: H Q,l (k) = ∑ j|Q j=l G j (k) ∑ j|Q j=l 1. ( 4 ) With a similar procedure, relative membership to each tranquility viewpoint group was also extracted for the participants from each park separately.
In Figure  2 , the results are therefore shown as the relative membership to tranquility viewpoint groups, i. e. , the average of the normalized degree of similarity calculated by Equations (  3 ) and  (4) , for different answer categories on the questions about hearing particular sounds. This may look slightly unconventional, as it is more popular to show expected effects as a function of expected cause. Given that the descriptive analysis shows some possible trends, the statistical significance was assessed using the Kendall rank correlation coefficient (τ b ) (statistical analyses were performed using the Python programming language with Scipy [33] (analysis of variance (ANOVA) and correlation) and Statsmodels  [34]  (Tukey test) modules). Table  2  shows this correlation coefficient between noticing sounds belonging to one of the three categories (human sounds, natural sounds, mechanical sounds) and the relative adherence to the tranquility viewpoint groups ("Social relationships", "Natural sound sources", "Silence").
Question A Tukey post hoc test revealed that the relative membership of people belonging to tranquility groups "Silence" (H = 1. 195, SD = 0. 088, p = 0. 006) and "Natural sound sources" (H = 1. 164, SD = 0. 076, p = 0. 014) was statistically significantly different from the relative membership of "Social relationships" group (H = 0. 872, SD = 0. 050). In brackets, H represents the relative membership to the stated tranquility viewpoint group (Equation (  4 )), whereas SD represents the standard deviation of the dataset used to calculate this relative membership (Section 3. 6). Additionally, no statistically significant difference could be found between the relative memberships to tranquility groups "Silence" and "Natural sound sources" using the Tukey test. These statistically significant trends reject the hypothesis that persons associating tranquility to social relationships would report hearing human sounds more often as well as the hypothesis that persons associating tranquility to natural sound sources would report hearing natural sounds more often as they would pay more attention to noticing these sounds.
541, p < 0. 001). A Tukey post hoc test unveiled that the difference was statistically significant for all comparisons. In particular, statistical significance of p less than 0. 001 was identified between "Natural sound sources" (H = 1. 049, SD = 0. 040) and "Silence" (H = 1. 269, SD = 0. 048) tranquility viewpoint groups as well as "Social relationships" (H = 0. 893, SD = 0. 034) and "Silence". In brackets, H represents the relative membership to the stated tranquility viewpoint group (Equation (  4 )) and SD represents the standard deviation of the dataset used to calculate this relative membership (Section 3. 6). Additionally, relative membership to "Natural sound sources" (H = 1. 049, SD = 0. 040, p = 0. 019) was found to be statistically significantly higher than to "Social relationships" (H = 0.
Acknowledgments: The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme   FP7/2007-2013/ under REA Grant agreement 290110, SONORUS "Urban Sound Planner" , and from the  Research Foundation Flanders (FWO-Vlaanderen)  under Grant  G0D5215N ,  ERC  Runner-up project  MAESTRO. Furthermore ,  Michiel Boes  is a doctoral fellow of the  Research Foundation Flanders ; the support of this organization is gratefully acknowledged.
ISSUE 1. How to account for the gold standard When establishing a clinical tool, the ultimate objective is to be useful for routine clinical practice. Under this premise, our algorithm was trained and tested on data whose labels were based on behavioral evaluation of all patients admitted in neurological service for the evaluation of their conscious state. In that sense, we did not limit our dataset to specific characteristics. Behavioral assessment of consciousness, however, is not entirely accurate to diagnose patients and approximately 15% of the patients behaviorally classified in VS/UWS are expected to retain some conscious processing  (Stender et al. 2014). In that case, one should consider that a 100% prediction rate of the clinical labels probably represents overfitting of the used dataset.
ISSUE 2. Generalization and replicability Using this tool, we validated our results using different recording conditions that match the clinical EEG. The classification performance remained unchanged to different conditions, such as when varying the sampling frequency (125 and 250 Hz), the total number of number of trials (10-500), and channels (8-256). A similar procedure was followed to test the validity under different recording conditions, this time by training the classifier using auditory stimulation recordings and testing on pure resting-state recordings  (Engemann et al. , n. d. , manuscript in preparation). In summary, these findings show that EEG can produce generalizable predictive models, sensitive to different recording conditions. The capacity of integrating data across a wide range of situations facilitates the evaluation of the state-ofconsciousness in both nonspecialist multisite clinical settings and specialised research facilities.
Testing our metric on an independent dataset of subjects able to report -immediately or retrospectively -the presence/absence of consciousness clearly offers the advantage of avoiding the issue of reasoning in a circular manner, i. e. assessing the performance of a measure of consciousness using the target population as a gauge for the accuracy of the measure in the absence of a veridical benchmark of the true state-of-affairs. Furthermore, the inclusion in our benchmark population of subjects that were behaviorally unresponsive and disconnected from the external environment while conscious, as well as conscious brain-injured patients, allows for the best available approximation of the challenging conditions presented by the unresponsive brain-injured patients.
The quantification of fMRI resting state functional connectivity in patients can be challenging. When employing datadriven independent component analysis (ICA), for example, a dataset is divided into maximally different statistical components and, thus, it is able to isolate cortical connectivity maps from non-neuronal signals  (Beckmann et al. 2005). This method allows us to evaluate and compare the coherence of activity in multiple distributed voxels and hence identify more brain networks other than the DMN  (Heine et al. 2012). However, ICA does not provide any ordering of the independent components, which can make the data representation difficult to interpret. A common solution to identify the network of interest is to calculate the fit between the components' spatial pattern and the pattern of a pre-defined template which represents the network  (Greicius et al.
To avoid double-dipping, we validated the most highly ranked network on two independent clinical datasets and across healthy controls scanned in different centers. Single-patient classification was performed based on the connectivity strength of the "auditory network", which was ranked most highly among all studied systems in separating MCS and VS/UWS patients. Based on this network's connectivity, 20 of the 22 new patients were classified congruently, namely the clinical diagnosis matched the classifier's decision (Fig. 6A ). It should be noted that this network was referred to as "auditory" for the sake of consistency with the literature. In fact, apart from temporal cortices, this network encompasses regions in occipital cortex, pre-and postcentral areas and insula. Hence, the discriminative feature of this system was connectivity in areas beyond audition-related, such as occipital and bilateral opercular/insular cortex (Fig.
ISSUE 1. Determining the gold standard: multimodal assessment In order to ensure that the proposed metric reflects the construct which is supposed to reflect (i. e. a connectivity pattern  discriminating the conscious state of MCS from that of VS/ UWS), a well-defined diagnostic baseline was essential. To that end, the classifier's trainset included data of patients who underwent a multimodal assessment of their conscious state by means of behavioral evaluation and neuroimaging. Patients were repeatedly examined with standardized clinical tools (an average of six assessments per patient with the CRS-R by multiple assessors). This clinical diagnosis was further crossvalidated with FDG-PET imaging, which has been shown to have high sensitivity in identifying patients in MCS  (Stender et al. 2014).
Figure5. The fMRI functional connectivity pattern which was used as a feature to differentiate MCS from VS/UWS patients. Areas in occipital and bilateral opercular/insular cortex were more functionally connected in patients in MCS compared to those in VS/UWS. A support vector machine classifier was trained on this pattern with data collected in one center, and it was generalized on an independently dataset of patients evaluated at different sites. The algorithm's classification was congruent with the clinical evaluation of consciousness in 20/22 assessed patients.
Acknowledgements This work was supported by the  James S. McDonnell   Foundation Scholar Award 2013 , the  Institut national de la santeet de la recherche medicale (INSERM) , the  EU 2020 Research and Innovation Programme  under Grant Agreement No. 720270  ( HBP SGA1 ), the  Institut du Cerveau et de la Moelle Epiniere (ICM, Paris)  and the  STIC-AmSud  grant (Complex) awarded to JS.
Our study provides a quantitative framework to also investigate the spectrum of EEG signals. Specifically, our main conclusion that the spatiotemporal characteristics of the underlying rhythm plays an important role in the final filtering produced by measuring, can be of importance in EEG studies. Interestingly, recently proposed methods for analyzing EEG signals leverage spatial relationships between EEG sites for frequency recognition. In  [35]  spatial filters are built from EEG training datasets to provide reference signals which are needed for frequency recognition in steady-state visual evoked potentials. Their results show that the proposed method improves frequency recognition, compared to conventional methods that do not use any spatial information. This supports the takehome message that by incorporating prior knowledge about the underlying degree of spatiotemporal correlation, spectral features can potentially be extracted with higher accuracy.
FACS For aNPC sorting experiments, cortices of e14. 5 control and mutant were processed in parallel. Single cell suspensions were produced using the MACS Neural Tissue Dissociation kit containing papain (MiltenyiBiotec, Paris, France) following the manufacturer's instructions. Cell viability (typically 495%) was assessed with Trypan blue staining. Cell surface staining of CD133 (prominin1) was performed on cell suspensions with rat anti-13A4-APC conjugated (1:100, eBioscience, Brussels, Belgium, Clone 13A4). Cells were incubated with CD133 antibody on ice for 60 min in PBS (1000 μl final volume per brain). Rat IgG-APC (1:100, eBioscience) was used as isotype controls. Cells were sorted according to their APC staining. FACS were performed on a BD FACSAria III sorter (BD Bioscience), and analyzed using the FACS Diva software (BD Bioscience).
ACKNOWLEDGMENTS We thank  Dr Xavier Morin  for providing the pcx-Gfp plasmid,  Catherine Lombard  for her help with the UCL-IREC FACS platform and  Valérie Bonte ,  R El Kaddouri ,  I Lambermont  and  E Paitre  for technical support. This study made use of data generated by the  DECIPHER community. A full list of centers who contributed to the generation of the data is available from http://decipher. sanger. ac. uk and via email from decipher@sanger. ac. uk. Funding for the project was provided by the  Wellcome Trust. This work was supported by the following grants:  FNRS PDR T0002. 13 , FNRS PDR T00075. 15 , Interuniversity Poles of Attraction (SSTC ,  PAI p6/20  and  PAI7/20 ),  Fondation Médicale Reine Elisabeth and Fondation JED-Belgique. FT is a Research Director of the  Belgian Fund for Scientific Research (FNRS).
All participants gave their informed consent to participation in the study, which had been approved by the local institutional review boards (CPP Nord-Ouest IV, 2012-A 01317-36, ClinicalTrials. gov Identifier: NCT01792843). Detailed demographic and disease-related variables were recorded. All the patients' medications were checked and doses of antiparkinsonian medication were converted to levodopa equivalent daily dose according to the algorithm by  Tomlinson et al. (2010). Severity of motor symptoms was assessed by the score at the Movement Disorders Society -Unified Parkinson Disease Rating Scale (MDS-UPDRS)part III  (Goetz et al. , 2008)  and disease stage by the Hoehn & Yahr score  (Hoehn and Yahr, 2001). The severity of depression, apathy and anxiety symptoms was quantified with the 17-item Hamilton Depression Rating Scale  (Hamilton, 1960) , the Lille Apathy Rating Scale  (Sockeel et al.
Data acquisition and preprocessing Dense-EEG were recorded with a cap (Waveguard®, ANT software BV, Enschede, the Netherlands) with 128 channels including 122 scalp electrodes distributed according to the international system 10-05  (Oostenveld and Praamstra, 2001) , two electro-cardiogram and four bilateral electro-oculogram electrodes (EOG) for vertical and horizontal movements. Electrodes impedance was kept below 10 kΩ. The data were collected in an eye-closed resting-state condition for 10 min with the software BrainVision Recorder (BrainProducts®). Subjects were instructed to do nothing and relax. All recordings were performed between 11:00 and 12:00 a. m. to limit drowsiness. An investigator controlled online the subject and EEG and verbally alerted the subject every time there were signs of drowsiness on the EEG traces or in behavior.
Signals were sampled at 512 Hz and band-pass filtered between 1 and 45 Hz. Channels and epochs containing artifacts were automatically and/or manually discarded. The automatic procedures included EOG artifact detection and correction using the method developed in  (Gratton et al. , 1983) , and EEG artifact analysis using a data inspection tracking system to remove data with an amplitude over 90 μV. The automatic selection was confirmed manually and epochs with remained artifacts (such as movement artifact) were removed. For each participant, we selected the maximum number of four-second segments artifacts-free to perform analyses. An atlas-based approach was used to project EEG sensor signals onto an anatomical framework consisting of 68 cortical regions identified by means of the Desikan-Killiany  (Desikan et al.
Functional connectivity analysis Functional connectivity matrices were computed using the 'EEG source connectivity' method  (Hassan et al. , 2014; Hassan et al. , 2015b). It includes two main steps: i) solving the EEG inverse problem Fig. 1. Structure of the investigation. Patients were categorized by their cognitive performance 1) cognitively intact subjects, 2) patients with mild cognitive impairment and 3) patients with severe cognitive impairment. The demographic and clinical features of the three groups are summarized in Table  1. The performance and the neuropsychological test of the three groups are also described in Table  2  (see  (Dujardin et al. , 2015)  for more description about the database). Data: Dense-EEGs were recorded using 128 electrodes during resting state (eye closed).