dataesr/scientific-paragraphs-categorization huggingface dataset:
A chlorided silver wire, placed in the cerebellum or visual cortex, served as a ground electrode. Isoflurane (0. 5-2%, 0. 2-0. 4 L/min) was administered via mask adapted to the animal head leaving an access to the whiskers at the contralateral whisker pad. The level of surgical anesthesia was assessed with an alligator clip applied to the tail. The data presented for each isoflurane concentration were acquired >15 min after a change in isoflurane concentration.
Data analysis Raw data were preprocessed using a custom-developed suite of programs in Matlab analysis environment. Wideband signal was downsampled to 1000 Hz and used as LFP signal. Positive polarity is up throughout all manuscript. For spike detection, wide-band signal was highpass filtered (>400 Hz) and negative events exceeding three standard deviations in amplitude were considered as spikes. Sensory-evoked potentials (SEPs) were detected as the first troughs of sensory-evoked responses in L4. For the analysis of sensory-evoked oscillations, 500 msec periods following SEP were used. For baseline activity assessment periods 200 msec prior to stimulus were used. LFPs, extracellular units, and intracellular data were analyzed by custom-written, MATLAB-based programs. Spectral analysis was carried out using Chronux toolbox procedures (http://chronux.
Sharp potentials were defined as single LFP deflections exceeding 10 standard deviations in amplitude with more than four spikes inside. Current source density (CSD) analysis across the cortical depth was used to eliminate volume conduction and to localize synaptic currents. 45 CSD was computed for each recording site according to a differential scheme for second derivative and smoothed with a triangular kernel of length 3. Matlab code for EEG analysis is freely available on demand from Dr. Andrei Zakharov at mphiszav@rambler. ru.
Acknowledgments We thank  Veronique Brevaut  for the valuable comments. This study was supported through the  Agence Nationale de la Recherche  (grant  ANR-09-MNPS-006  to R. K. ), the  Government of the Russian Federation  (grant to leading scientist  11. G34. 31. 0075  to R. K. ) and  Fondation pour la Recherche Medicale  ( DEQ20110421301  to R. K. ).
e. , minimal electric current of 500 ms duration necessary to produce a spike) and first-spike latency in TAFA4 GFP/+ (n = 15) and TAFA4-null (n = 13) GFP + neurons is shown. Data are determined as in (E). (I) Stimulus-frequency relationships in TAFA4 GFP/+ (n = 7) and TAFA4-null (n = 7) neurons using 500 ms depolarizing current injections are presented. See also Figure  S2. innervate hair follicles in the periphery and project to the innermost layer of lamina II centrally. Human psychophysical studies have implicated C-LTMRs in two distinct features of mechanical sensation: sensation of pleasant touch, and sensation of vibration-evoked pain  (Lo ¨ken et al. , 2009; Nagi et al. , 2011). In the mouse, VGLUT3 loss of function specifically impairs mechanical hypersensitivity following inflammation, nerve injury, and trauma, indicating a critical role of C-LTMRs in the mechanical hypersensitivity caused by injury  (Seal et al.
e. , minimal electric current of 500 ms duration necessary to produce a spike) and first-spike latency in TAFA4 GFP/+ (n = 15) and TAFA4-null (n = 13) GFP + neurons is shown. Data are determined as in (E). (I) Stimulus-frequency relationships in TAFA4 GFP/+ (n = 7) and TAFA4-null (n = 7) neurons using 500 ms depolarizing current injections are presented. See also FigureS2.
ACKNOWLEDGMENTS We are grateful to the  CIML transgenic core facility  for generation of tafa4-GFP KI chimeric mice. Thanks to  L. F. Reichardt  for providing TrkA antibody. We also thank  N. Laleve  ´e and the members of the laboratory for scientific discussions, IBDML imaging, and animal facilities for assistance. A. Mantilleri  was a  BDE-Region (Region Provence-Alpes-cote d'Azur) fellow cosupported by Bioseb. This work has been funded by  ANR-Nociceptor diversity  and  ERC-Starting  grant paineurons  260435.
2D ). By late L3 and pupal stages, size differences between wild-type and mutant siblings are obvious (Fig. 2C ). In addition, the onset of both pupariation and adult emergence are delayed in dGPHR null flies compared to wild-type controls (Fig. 2D ). Thus, the smaller body size of dGPHR mutants is likely to be caused by slower growth rate during the late larval period rather than by precocious pupariation. Note that few dGPHR mutants can reach pupal (20%67 SD of expected pupae) and adult (5%62 SD of expected adults) stages. To test whether this overall size reduction was due to a decrease in cell size, cell number or both, we quantified these parameters in adult wing blades. As shown in Fig. 2E , both cell size and cell number were reduced in dGPHR mutant when compared to controls. The similar penetrance and strength of the phenotype observed between dGPHR homozygous mutants and dGPHR k34 /Df(2R)ED2426 or dGPHR LL03674 /Df(2R)ED2426 allelic combinations (data not shown) demonstrate that dGPHR k34 and dGPHR LL03674 are complete loss-of-function alleles as far as growth control is concerned.
The vertebrate GPHR proteins are the functional orthologs of dGPHR The differential subcellular localization of GPHR in human and Drosophila cells prompted us to test the functional relationship between the vertebrate and invertebrate GPHRs. For that, we asked whether the vertebrate GPHR protein could functionally compensate for the lack of dGPHR function in flies. To do so, the human or the hamster GPHR proteins were expressed a in dGPHR mutant flies using a ubiquitous Gal4 driver (daughterless-Gal4) and the weight of the emerging adult flies was used as a readout of body size. As shown in Fig. 5A , expression of the dGPHR protein or its two vertebrate orthologs was sufficient to fully rescue dGPHR mutant growth defects and developmental lethality (data not shown). This indicated that the vertebrate GPHRs can compensate for a lack of dGPHR and that these proteins are therefore functional orthologs.
ACKNOWLEDGMENTS The preparation of this manuscript has been funded through the  European Community's "Seventh Framework "  Programme  ( FP7 ) for an innovative scheme, the  Innovative Medicines Initiate (IMI)  under Grant Agreement no. 115009  (" Prediction of cognitive properties of new drug candidates for neurodegenerative diseases in early clinical development "; Pharmacog). IMI is a public-private partnership founded in 2008 by the pharmaceutical industry (represented by the  European Federation of Pharmaceutical Industries and Associations, EFPIA)  and the  European Communities  (represented by the  European Commission ).
BCI feedback also does little to help the user feel competent at BCI control. More importantly, BCI is non-specific since it does not explain why or what was good or bad about the task performed by the user. With the machine learning approach, BCI feedback might also be unclear and meaningless, if it is based on a classifier trained on incorrectly performed mental tasks. Unfortunately, this situation is likely, since first time users have by definition never used a BCI before, and thus cannot be expected to perform the required mental tasks perfectly from the start. In other words, for new BCI users who cannot do the mental task correctly from the start, the feedback will indicate them they have done well if they performed the mental task as badly as they did the very first time, during the calibration data collection.
PATIENTS AND METHODS Study participants and data collection We performed a retrospective analysis of patients with spontaneous ICH (defined as ICH without evidence for an underlying cause, except for sporadic small vessel disease) referred to four specialist stroke centres over defined time periods in the UK and Belgium. The hospitals were: University College London Hospitals National Health Servie (NHS) Foundation Trust (09/ 2004-06/2011), Cliniques Universitaires Saint Luc (Brussels) (12/2004-04/2008). At all participating centres, MRI scanning is a routine investigation for ICH, unless there are contraindications. Cases were systematically ascertained using multiple overlapping methods from prospective clinical databases and radiological reports. A total of 377 patients with ICH were screened, of whom 129 were excluded because of diagnosis of nonspontaneous ICH (eg, secondary to aneurysms, tumours, cavernomas, etc), and 127 because MRI with the necessary sequences of adequate quality was not available for analysis.
We categorised cases of spontaneous ICH as either strictly lobar, consistent with a diagnosis of CAA (clinically possible or probable CAA according to the Boston criteria  9  ) or 'other ICH' (ie, deep ICH and mixed deep and lobar ICH). Demographic and clinical information (eg, age at time of MRI, gender, vascular risk factors, use of antithrombotics, etc) was obtained from prospective databases and by medical records review using standardised data collection forms. Hypertension was defined as a history of hypertension, taking antihypertensive treatment or documented elevated blood pressure (systolic >150 or diastolic >95 mm Hg) prior to ICH, diabetes as ongoing use of a hypoglycaemic agent, and smoking as history of tobacco use before admission. The study received ethical approval by the National Hospital for Neurology and Neurosurgery and Institute of Neurology Joint Research Ethics Committee, the Commission d'Ethique Biomedicale Hospitalo Facultaire of the Faculte de Medicine (Universite Catholique de Louvain), and the Comite d'ethique medicale of the Cliniques Universitaires UCL de Mont Godinne.
Acknowledgements  Dr Andreas Charidimou  receives research support from the  Greek State Scholarship Foundation , the  Stroke Association  and the  British Heart Foundation. Dr Rolf Hans Lager  receives support by the  Samantha Dickson Brain Tumour Trust  and the  Brain Research Trust. Dr David J Werring  is supported by the  Department of Health/Higher Education Funding Council for England  ( Clinical Senior Lectureship Award ), the  Stroke Association  and the  British Heart Foundation. Part of this work was undertaken at  UCLH/UCL  who received a proportion of funding from the  Department of Health's NIHR Biomedical Research Centres funding scheme.
The Community Core Vision (CCV) library processes the camera image and outputs finger tracking data. To create the layer of light, a row of 48 infrared LED's was attached slightly above the strip board along the left and right sides of the board as shown in Figure  5. Because the paper strips are flat and below the level of the infrared light, they are not detected and do not interfere with touch detection. However, the surface of the strip board itself does cause multiple false touches along the edges of the ridges. Fortunately, the bands where these false touches occur are narrow and are found in areas where the user does not need to touch. Thus, in software, we are able to ignore any touches that originated within one of these bands.
Acknowledgements Work supported by the  CNRS , the  ANR  ( Complex-V1  project) and the  European Community  ( BrainScales  project,  FP7-269921 ). Published: 8 July 2013 doi: 10. 1186/1471-2202-14-S1-P106Cite  this article as: Bedard and Destexhe: The electric properties of extracellular media affect cable properties of neurons. BMC Neuroscience 2013 14(Suppl 1): P106.
Measurements Anthropometric and Cardiovascular Data Medical examination and anthropometric measurements for each subject were performed by a pediatrician. Body mass was measured to the nearest 0. 1 kg on a digital scale (OHAUS, Florham Park, NJ, USA). Height was measured with a standing stadiometer and recorded with a precision of 0. 1 cm. The BMI was calculated as body weight (kg) divided by the square of height (m). The Z-score for BMI was calculated according to the formula given by Rolland-Cachera et al. [26]. The percentage of body fat was calculated using the equation of Slaughter et al. [27]  as described in the following: If triceps and subscapular skinfolds are <35 mm: Boys: %BF = 1. 21 (sum of 2 skinfolds) 2 -0. 008 (sum of 2 skinfolds) -1. 7 Girls: %BF = 1. 33 (sum of 2 skinfolds) 2 -0.
Acknowledgment. Ideas discussed here are based in part on a presentation of the author at the 2012 Journee of the group lipid nutrition. This work was supported by a  ATIP grant  from the  Centre National de la Recherche Scientifique (CNRS)  and a grant by the '' Agence Nationale de la Recherche ''  ANR-09-BLAN-0267-02. CC received a PhD fellowship from the  CNRS  and a research fellowship from the  Soci et e Francophone du Diab ete-Roche (SFD).
Acknowledgments The authors would like to thank the  Laboratory of Bioclinical Radiopharmaceutics (LRB)  for providing rats. The authors also thank the  Interdisciplinary Energy program  of  CNRS PR10-1-1 , the  ANR   Emergence-2010 EMMA-043-02 , and the  ANR Investissements d'avenir -Nanobiotechnologies   10-IANN-0-02  programmes for financial support. This work was supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir programme (Labex CAMI)  under reference  ANR-11-LABX-0004. Arielle Le Pellec  is greatly acknowledged for technical assistance.
Proteins may be related to each other very specifically as homologous subfamilies. Proteins can also be related to diverse proteins at the super family level. It has become highly important to characterize the existing sequence databases by their signatures to facilitate the function annotation of newly added sequences. The algorithm described here uses a scheme for the classification of odorant binding proteins on the basis of functional residues and Cys-pairing. The cysteine-based scoring scheme not only helps in unambiguously identifying families like odorant binding proteins (OBPs), but also aids in their classification at the subfamily level with reliable accuracy. The algorithm was also applied to yet another cysteine-rich family, where similar accuracy was observed that ensures the application of the protocol to other families.
Methodology Datasets 7 structural entries of odorant-binding proteins (OBPs; PDB ID: 1dqe, 2wcj, 2gte, 2erb, 3k1e, 3bfh, 1ow4), available then, were used for the construction of the structural alignment. The dataset used in this analysis is comprised of 116 conotoxin sequences  24  and 284 odorant binding proteins from mosquito genomes. 26 The conotoxins are classified into 7 classes. The odorant binding proteins are classified into 3 major classes including Classic, PlusC and Atypical; the Atypical are further divided into 4 subtypes (MAtype1-4). Representative sequences were chosen from the different classes for the construction of the training profile and the other sequences were used in the test set (Table  1 ).
Dayhoff matrix-based scheme: For each position in the query sequence, the score is calculated as the product of probability of each amino acid occurring at that position in the template and the Dayhoff Matrix score for the amino acid substitution from that AA to the residue present in the query. Finally, the scores are averaged for all the 12 positions. However, this matrix of amino acid exchanges are recorded and normalized as observed for large numbers of unrelated protein families and are also not position-specific in nature. Given a query string Q with amino acid Qi at functional position i, where 0 # i # p and a training profile T which is an alignment with i functional positions. The scores according to the different schemes are defined as follows: Majority based score: Probability_1 based score: Is Equal P Q m p Is Equal P Q m if P Q m o i i i i p i i i i i i ( ( ), ) ( ( ( ), ) ( ) = ∑ = = 1 1 × t therwise 0) M T Q p ij i j n i p ( , ) = = ∑ ∑ 1 1 Probability_2 based score: Functional residue distance-based scoring scheme C β -C β distances of the residues at the functional positions were calculated from the models of 131 classic OBP sequences (data not shown).
Functional sites and fuzzy functional template Functional residues of proteins involved in ligand binding are generally conserved through the evolution of proteins and generally considered as good classifiers of protein families and for function annotation. 13 The ligand-binding residues from the bound complexes of the available PDB entries were mapped to the structural alignment generated by COMPARER. 27 or the family of insect odorant binding proteins, the positions of the alignment, which had ligandbinding entries in at least 4 of the 7 PDB entries, were considered to be significant functional residue positions. 12 such positions were considered to be components of the functional template (Fig. 1 ). The C β -C β distance between these 12 residues were calculated and averaged in the form of a matrix called the 'fuzzy functional template' (FFT).
Functional residue-based scoring scheme The 'PROB_2' scoring scheme, with the addition of homologues, achieves the best range and correlation. The scores were based on the occurrence, probability of occurrence and Dayhoff matrix as described in the Methods section. For the family of insect odorant-binding proteins, different training datasets were analyzed that include (1) a 7-member training set, which is the initial structure alignment, (2) a 25-member dataset where the 7-member dataset was populated (to include evolutionary data) with one additional close homologue from each of the mosquito genomes to every member in the 7-member dataset, (3) a 5-member dataset where the 2 mosquito crystal structures 2erb and 3k1e were removed to avoid potential bias in scoring the models (since these 2 structures served as templates for modeling) and (4) an 18-member dataset from which the 2 mosquito crystal structures and their homologues were excluded.
The range of scores for each of the methods on every training set were analyzed and it was observed that the probability score PROB_2 achieved the best range, followed by the majority-based scores (Table  2A ), and that they also achieved the best correlation when compared to other 2 methods (Table  2B ). It was also observed that addition of homologues to the initial dataset significantly improved the range and correlation. All 12 positions in the scoring scheme are equivalent in importance It was important to analyze whether certain functional site positions contributed more to the scores in order to provide different weights on the positions. This was done by jack-knifing each of the 12 individual positions and recalculating the scores for the initial 7-member dataset. The Pearson correlation coefficient between the scores were calculated after removing each of the 12 residue positions (Table  3 ) and it was observed that the removal of any one position from the scoring scheme did not significantly alter the scores.
The scores are independent of the sequence identity of the query sequence with the template Since the scoring scheme is based on the probability of occurrence of an amino acid, the effect of sequence identify on the scores had to be considered carefully. A histogram of the number of sequences versus the sequence identity of the protein with the closest structural template in the dataset was plotted (Fig. 3 ). The distribution of the graph indicated that the scores are indeed independent of the sequence identity. A histogram of the number of sequences versus the percentage sequence identity of the query sequence with the template was plotted and the consistently high-scoring and low-scoring sequences were marked on it (Fig. 4 ). It was observed that the distribution of the low scoring and high scoring queries was independent of sequence identity.
Comparison of the sequence-based scoring scheme with sequence searches and phylogenetic analyses We find that our simple sequence-based objective scoring scheme works better than domain-based subfamily association or phylogeny-based associations; for example, in the case of odorant binding proteins, which fall into three major subfamilies the Classic PlusC and Atypical as described earlier in the manuscript. When each of these members are searched against the conserved domain database it is observed that in many cases cross-talk is seen with respect to subfamily (Supplementary Table  1 ). For example, most of the Plus C Obps are never identified to carry the PBP_GOBP domain, and atypical OBPs,  which should be predicted to have two PBP_GOBP domains, are predicted to have only 1 PBP_GOBP domain.
This was analyzed by observing the cysteine conservation patterns of sequences in the test datasets when aligned to profiles that were constructed using a training set of each of the classes described above. A training set for the 7 different classes of OBPs (disulphide profiles) was prepared (as summarized in Table  1A ). A representative sequence was identified from a phylogeny of odorant binding proteins of each class. For the Minus-C class, the same profile for Classic OBPs was used, but only the 1st, 3rd, 4th and 6th cysteine positions were considered. A composite classification scheme was devised for the family of OBPs incorporating the 7 different scores and the length of sequence as attributes. The protocol was applied to a dataset of 284 mosquito OBP sequences (from Anopheles gambiae, Aedes aegypti and Culex quinquefasciatus) and the class predictions were compared with the predictions of class association independently made from phylogenetic analysis.
30 The prominent disulphide connectivity patterns in the 4 major families of conotoxins are shown in Supplementary Figure  5 , and these alone were used for scoring purposes. A classification scheme was developed for conotoxins as shown in Supplementary Figure  3 , incorporating the 4 scores corresponding to each of the 4 major families. The classifier (constructed using the training set as shown in Table  2 ) was tested on a dataset of 116 conotoxin sequences obtained from Mondal et al  24  and the predictions made by the scheme were compared with the known classes of the sequences in the study by Mondal et al. 24  The scheme gave an accuracy of 93. 1% for the test set and the confusion matrix is presented in Figure  5B. The re-substitution test on the training set provided an accuracy of 100% for all 4 families.
conclusion Simple domain-finding techniques such as association to Pfam families, can be helpful only to relate mosquito OBPs to the broad family of 'odorant binding proteins' (PF01395), but cannot be distinguished as Classic, PlusC and Atypical odorant binding proteins. These subfamilies differ in their sequence features, even though they carry the basic PBP/GOBP domain. In the case of families where the sequence divergence in very high, it is important that family-specific classification methods are derived to obtain a more meaningful functional classification of the family. Evolutionarily-constrained functional and structural entities/signatures, combined with family-specific profile-based scoring, improve the function annotation quality and can also be further extended to a subfamily level classification.
Trained using trypin dataset Trained using Thrombin dataset Trained using plasminogen dataset Majority probability Dayhoff Majority probability Dayhoff Majority probability Dayhoff 0. 688571 0. 662976 0. 68131 0. 666786 0. 68131 0. 68131 0. 68131 0. 688571 0. 688571 0. 683095 0. 688571 0. 688571 0. 688571 0. 688571 0. 591548 0. 574524 0. 574524 0. 574524 0. 574524 0. 574524 0. 584881 0. 579762 0. 589405 0. 576071 0. 574524 0. 594643 0. 530238 0. 527143 0. 584286 0. 60631 0. 415357 0. 436667 0. 597381 0. 344286 0. 219167 0. 575952 0. 52131 0. 538571 0. 629881 0. 509286 0. 535595 (Continued) 0. 946667 0. 88 0. 906667 0. 88 0. 906667 0. 906667 0. 906667 0. 946667 0. 946667 0. 92 0. 946667 0. 946667 0. 946667 0. 946667 0. 706667 0. 68 0. 68 0. 68 0. 68 0. 68 0. 666667 0. 706667 0. 64 0. 64 0. 68 0.