dataesr/scientific-paragraphs-categorization huggingface dataset:
trypin dataset Trained using Thrombin dataset Trained using plasminogen dataset Majority probability Dayhoff Majority probability Dayhoff Majority probability enhanced function annotation of proteins 0. 581309 0. 604167 0. 568452 Dayhoff 0. 328214 0. 35131 0. 588095 0. 373095 0. 586786 0. 572024 0. 597381 0. 584881 0. 383571 0. 597381 0. 454762 0. 577738 0. 480357 0. 570833 0. 56619 0. 577738 0. 536905 0. 586429 0. 432619 0. 566548 0. 38 0. 591071 0. 189286 0. 622976 0. 584048 0. 568095 0. 506905 0. 577738 0. 465357 0. 596905 0. 577857 0. 603929 0. 594524 0. 591905 0. 560357 0. 56119 0. 590238 0. 025714 0. 591071 0. 599643 0. 574167 0. 6075 0. 5125 0. 565 0. 53119 0. 557619 0. 586429 0. 583929 0. 588095 0. 55631 0. 5875 0. 590238 0. 58619 0. 424286 0. 589762 0. 595833 0. 595 0. 584643 0. 600833 0.
Patch pipettes (3-5 M⍀), for wholecell current, were filled with internal solution containing the following: 115 mM potassium gluconate, 20 mM KCl, 1. 5 mM MgCl 2 , 10 mM phosphocreatine, 10 mM HEPES, 2 mM magnesium ATP, and 0. 5 mM GTP (pH 7. 2, 285 mOsm). Whole-cell recordings were performed using aCSF at 34°C (flow rate ϭ 2. 5 ml/min). Blue light trains (20 Hz for mPFC or phasic 20 Hz, 40 ms for VTA) were generated by a stimulator connected via a FC/PC adaptor to a 473 nm blue laser diode (OEM) and delivered to mPFC and VTA slices via a 200 m optical fiber. Current-clamp experiments were performed using the Multiclamp 700B amplifier, and data acquisition was performed in pClamp 10 (Molecular Devices). Series resistance was monitored during the experiments, and membrane currents and voltages were filtered at 3 kHz (Bessel filter).
The next day, sections were rinsed in PBS followed by a 1 h incubation in secondary antibodies: donkey anti-rabbit Cy3, donkey anti-mouse Cy5, and donkey antichicken DyLight-488 or Alexa-488 (Jackson ImmunoResearch Laboratories). For mCherry and tyrosine hydroxylase immunohistochemistry, experiments were performed as previously described  (Lobo et al. , 2010; Mazei-Robison et al. , 2011). Sections were rinsed in PBS, mounted onto slides, and coverslipped. Imaging and cell counting. Immunofluorescence was imaged on a Zeiss Axioscope or Olympus Bx61 confocal microscope. Cell counting was performed with ImageJ software. Images sampling bregma 1. 42-1. 1 of NAc (core and shell) and dorsal striatum were taken from 2 or 3 brain sections/animal (see Fig. 1A ). A total of 400 -500 cells were counted per brain region per mouse using 250 m ϫ 250 m images.
Cells were counted using ImageJ software similar to a previous study  (Lobo et al. , 2010). Approximately 400 -500 total NeuN cells were counted per brain region per mouse, and then the number of GFP ϩ , GFP ϩ :⌬FosB ϩ , GFP Ϫ , and GFP Ϫ :⌬FosB ϩ cells were counted in each region. Data were quantified as follows: (GFP ϩ :⌬FosB ϩ neurons ϫ 100%)/(total GFP ϩ neurons) and (GFP Ϫ :⌬FosB ϩ neurons ϫ 100%)/(total GFP Ϫ neurons). Statistical analyses were performed using GraphPad Prism software. Two-way ANOVAs followed by Bonferroni post tests were used for all cell counting analyses.
Thirty two patients (17M:15F age 19-65 years, mean ages of 40. 3 years and 41. 9 years) completed the follow up assessment after 12 months including the MRI and the full standardized physical and functional assessment: see Supporting Information in File S1 for details of loss to follow-up. Inclusion criteria for the patients were: identical genetic diagnosis with homozygous c. 826C. A FKRP mutations, ambulant for more than 50 metres, no ventilator requirements and the ability to lie flat with no contraindications for MRI scanning. Data were acquired locally at the four centers between June 2009 and April 2011, using pre-agreed standardized MRI and clinical and functional assessments with a standardized reporting method. A physiotherapy assessment was performed in all patients at the time of the MRI.
Each leg was imaged individually using a 1606160 matrix interpolated to 2566256, FOV 2006200 mm: in Paris, it was possible to scan both legs together at the same resolution using FOV 4486244 mm. The data was analyzed offline to produce separate fat and water images  [19, 20]  from which quantitative fat-fraction maps were produced by expressing the fat signal as a percentage of the total signal per voxel. (iii) Interpretation. The images were analyzed by a consultant neurologist (TW). The T 1 w images were assessed on an individual muscle basis and graded according to the scale published by Mercuri et al. [21, 22]  (Table  1 ), assessing 14 muscles in the leg (9 in the thigh and 5 in the lower leg) and the gluteus maximus muscle. The quantitative fat-fraction maps were analyzed by defining regions of interest (ROIs) encompassing the full cross-section of individual muscles on the separated water image at the midpoint of both the lower leg and thigh for both baseline and follow-up datasets in 14 muscles, avoiding partial volume effects at the boundaries.
Statistical Methods The Shapiro-Wilk test demonstrated that the fat fraction measurements were not normally distributed. The average fat fractions in the group for a given muscle were therefore characterised by the median, with minimum and maximum values quoted. Non-parametric statistical tests were used for analysis using the SPSS version 17. 0 software (IBM, USA). Comparison of fat-fraction changes between paired baseline and follow up scans were tested using the Wilcoxon signed-rank test. Comparison of the functional changes between baseline and follow up were tested using the Wilcoxon signed-rank test. Statistical significance was taken to be p,0. 05.
Quantitative Analysis -Dixon Technique A total of 14 muscles per patient, nine muscles at mid thigh level and five muscles at mid lower leg level, were analysed in all 32 patients at baseline and follow-up (Table  3 ). Gluteus maximus muscle data was not acquired with the Dixon technique. The distribution of muscle pathology was broadly similar at baseline and follow-up and showed a broad spectrum of severity (Figure  1 ): some muscles were more severely affected such as the biceps femoris long head muscle (median fat fraction 71. 6% at baseline), whilst others demonstrated mild involvement, as in the tibialis anterior muscle (median fat fraction 5. 5% at baseline). The fat-fractions in 9 out of the 14 muscles increased significantly from baseline to follow-up, these being the medial and lateral gastrocnemius muscle in the lower leg, and the sartorius, gracilis, semimembranosus, semitendinosus, vastus lateralis, rectus femoris and biceps femoris long head muscle in the thigh.
The project was mainly funded through the  MRC Centre for Neuromuscular Disease  (http://www. cnmd. ac. uk/). Diagnostic facilities at the  Newcastle Muscle Centre  are supported by the  National Commissioning Group (NCG)  for rare neuromuscular disorders. TW is funded by the  Muscular Dystrophy Campaign, UK. KGH is supported by a  MRC NIRG  ( G1100160 ). The authors would like to acknowledge the financial support of the  UCL/UCLH NIHR Comprehensive Biomedical Research Centre  and the  Association Francaise contre les Myopathies. Sara and Ludvig Elsass Foundation, Denmark for the MRI  funding of the Danish patients. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Professor Vissing  has given lectures for and received travel expenses from  Genzyme.
This work was supported by the  French National Agency for Research  ( n°ANR-08-MNP-026 ) and Dicyt 2012-AHK, Chile. A. E. and C. C. were supported by the  French Ministère de l'Enseignement Supérieur et de la Recherche , the  Université d'Auvergne and INSERM. A. S. W. was supported by grant from the  Région Auvergne-FEDER. X. P. was a recipient of a doctoral fellowship from  Inserm-Région Auvergne. P. M. was also supported by grants from la  Fondation pour la Recherche Médicale  (Contracts Equipe FRM 2005FRM , 2009)). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The working group produced a consensus proposal that went through a pan-European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, "episodes" (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization.
For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make possible the build-up of a multinational database, and it will help in training young neurophysiologists. KEY WORDS: Assessment, Database, Definitions, EEG, Semiology, Terms. The interobserver agreement in electroencephalography (EEG) interpretation is only moderate  (Van Donselaar et al. , 1992; Stroink et al. , 2006). The EEG signal has a high complexity. It depends on the intricate interplay between the activation of neural networks, localization and orientation  (Wong, 1998)  of the source (dipole), and its propagation throughout the brain (Lopes da  Silva & van Rotterdam, 1993; Scherg et al.
, 2006). However, the EEG recordings are much more complex than to be described by a list of binominal measures. Free-text formats are sufficiently flexible to reflect the various features extracted and weighted by the observers, but it can also lead to suboptimal assessment of the recordings: it does not prevent the observer from missing important features, it allows the use of terms not widely accepted (local terminologies), and makes it difficult to transfer the results of EEG assessment from one laboratory to another (for building of a multinational database). The objective of our endeavor was to construct software for characterizing EEG and ictal clinical events, where the physician chooses from predefined terms, simultaneously generating a report and filling information into a database.
We wanted to develop a reliable tool to improve the quality of EEG assessment, to increase interobserver agreement in reporting EEG findings, to promote exchange of knowledge between EEG centers, to construct a multinational database for further research projects, and to assist in education and training. There have been several successful attempts in medicine to standardize the evaluation and reporting of complex clinical and/or electrographic patterns, for example, the Unified Parkinson's Disease Rating Scale and the scoring of the polysomnography recordings using the American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events. Previous computerized EEG reporting systems have been published  (Aurlien et al. , 1999; Finnerup et al. , 1999). Robert R. Young, Keith H.
Chiappa, and their colleagues at Massachusetts General Hospital have in the 1980s used a locally developed software package for reporting EEG findings. Fixed terms could be selected and a report was semiautomatically generated (personal communication). Ronald Lesser and his colleagues at Johns Hopkins have been using a locally developed software package ("Reporter") since 1998 for reporting EEG findings. Since then they have prepared 38,000 reports using the software (personal communication). Although "Reporter" does not construct a formal database, terms in the EEG descriptions are standardized, so that "keyword" searches can be used to help with a variety of needs, including construction of a database. However, the previous attempts did not reach broad international acceptance. One of them seemed to be too time-consuming in the clinical practice  (Finnerup et al.
, 1999)  and it only built a database, but not a report. Harald Aurlien and his colleagues have been using software ("Holberg") for reporting EEG findings and generating a database since 1998, and a modified version of this software has been used for reporting all standard EEG reports at the Danish Epilepsy Centre since 2009. In total, >36,000 EEG studies have been reported using this software. The database that was automatically generated during the reporting made possible to address specific issues related to certain aspects of the EEG, and this led to three additional publications  (Aurlien et al. , 2004 (Aurlien et al. , , 2007 (Aurlien et al. , , 2009)). However, these software packages remained in local use only. Probably the reasons for failing to reach a wider acceptance were the very different needs and traditions in different countries.
, 1974; Committee, 1981; Daube et al. , 1993; Glimore, 1994; Noachtar et al. , 1999; Blume et al. , 2001; Flink et al. , 2002; AASM, 2007; ACNS, 2007)  as well as the terms described in authoritative EEG textbooks  (Ebersole & Pedley, 2003; Niedermeyer & da Silva, 2005). We added to them or modified them only when absolutely necessary, based on published evidence and/or the consensus of the group. Because video-EEG recordings contain data on seizure semiology too, we attempted to include this into the structured report. The definitions for the terms used in SCORE are attached as Supporting Information. In the software these definitions are directly available for each term. Based on the consensus statement, the SCORE software has been developed by a group of programmers at the Holberg EEG AS.
The programming work was organized and supervised by one of the authors (HA), and, during this process, the content of the software was repeatedly compared and synchronized with the consensus statement by another author (SB). The development of the software took 3 years. It was an iterative process, where successive versions were tested by scoring EEG recordings in the clinical practice, and the software was continuously corrected and adjusted. In total, >2,000 recordings were included in this process, and four major revisions were made, in addition to the numerous corrections and trouble-shooting. A free version of the SCORE software and a detailed guidance for users can be requested from the following home-page: http://holbergeeg. com. The software automatically generates a report and saves the scored features in a local database.
Results Table  1  shows the main elements of SCORE, constituting the flowchart of data evaluation and interpretation. Elements 1-4 can be filled in by the EEG technician (physiologist), and later checked by the physician who interprets the recording. In the future, connecting the SCORE software with the patient administrative system of the hospital/EEG department would help filling in these administrative data. Naturally, several recordings can be listed for each patient. Each recording has its own referral. Elements 5-12 contain the main features assessed during the process of reading the EEG recordings (grouped in the software under "findings"). The list is long, because it is meant to contain all clinically relevant aspects that can occur during a diagnostic EEG recording. However, the software was designed in such a way that the user does not have to waste time on those features that do not occur in the recording.
In other words, if an element/feature is not applicable for the recording to be described, one should not open it from the list. The user chooses the complexity of scoring according to the clinical setting and the recording. The last element contains the overall interpretation of the recording, where the physician specifies the diagnostic significance. When this is done, a report is automatically generated, and the features scored by the user are fed into the database. The terms in the main flowchart are defined in Appendix S1. Personal data, referral, and recording conditions SCORE is installed and run within the hospitals' information technology system. The users must ensure that the software is used according to the local regulations for security of personal data. An "anonymization" function will be available.
Scoring the EEG Elements 5-12 are grouped under the heading "findings" in the SCORE software (Table  1 ). While reading the EEG, the physician "scores" the relevant features of the recordings using these entries. It follows the way electroencephalographers describe the EEG recordings. The first two elements contain the features of the "ongoing" EEG activity during wake period ("background activity") and during drowsiness and sleep. Interictal findings depict all the graphoelements/ EEG patterns that are considered abnormal, and that are not part of the ongoing (background) activity, and that are not the EEG manifestation of a seizure/clinical episode or of the postictal period; the presence of interictal discharges does not necessarily imply that the patient has epilepsy. The element "episodes" contains the clinical and EEG features of the seizures and other clinical/ictal events.
Discussion We constructed the SCORE software for standardized assessment, interpretation, and reporting of EEG, based on a European consensus process. The software has been tested in the clinical practice, corrected, and adjusted. Generating a report automatically feeds the scored EEG features into the database. A free version of the software is available. We plan a revision of SCORE based on the incoming suggestions, and a broader, international consensus. In the current version of the software the database is produced locally. We plan to make an international database, where centers wishing to participate can upload their data, after appropriate processing (removing) of personal data. The legal background for data transfer has to be clarified before proceeding to this (rules and regulations unfortunately differ even within the European Union).
Such an international database would constitute a valuable tool for further research projects, as search criteria can be constructed to verify hypothesis or extract relevant information from the database. In addition, the software makes it possible to compare the scored features in the report with a scored "second opinion" from another laboratory on an EEG laboratory. This offers the tools for quality control and audit. SCORE will be helpful in bridging the gap between the classical method of visual analysis of the EEG and the advanced (computerized) analysis methods. The appropriate analysis tools can be attached to the corresponding elements of SCORE (for example quantitative EEG analysis method for "background activity"; source analysis methods for the "localization descriptor," and so on).
The electroencephalographer can enrich with these methods his armamentarium for the analysis and interpretation of EEG recordings in the clinical practice, by integrating their results in the standardized EEG report. Integration of SCORE with the patient administration systems of the hospitals is going to save considerable time and increase the feasibility. The terms/features used in SCORE are provided with a definition in the current version. The intention of the SCORE consortium is to provide (besides the definition) typical examples of EEG samples (screen shots) showing the various features. Therefore, in addition to the definition, an EEG sample will be accessible directly (from the feature in question) in the software-the user will be able to open this while scoring the EEG. We consider that this has remarkable potential in training neurophysiologists.
In addition to EEG, MEG data will be integrated for standardized analysis in collaboration with the European Clinical MEG Society (EMEGS). At present the following languages are available in the software: English, Chinese, German, Dutch, Norwegian, Turkish, and Danish. Translation into eight other languages is already in progress. One can score a recording using one language and print out the report in another language. Unfortunately most of the terms and features of the EEG report are still largely based on tradition, and systematic evaluations of their diagnostic significance are not yet available. An international EEG database would help in further, evidence-based evaluation (and ultimately selection) of the features traditionally included in the EEG report. Therefore, we plan a periodical revision of SCORE, based on these data, and on additional, incoming suggestions and comments.
Acknowledgments We would like to express our gratitude for the excellent comments and suggestions to:  Andr as Fogarasi ,  Arup Chattopadhyay ,  Bernhard Steinhoff ,  Candan Gurses ,  Christine Soufflet ,  Fabrizio Monti ,  Ioana Mindruta ,  Kristina Malmgren ,  Maria Peltola ,  Michael Koutroumanidis , P eter Hal asz,  Riitta Lees ,  Roland Flink ,  Serap Saygi ,  Victoria Fernandez ,  William Szurhaj , and  Zarko Martinovic. We are grateful to  Svenn Krossnes ,  Tom Eichele ,  Emanuel Neto ,  Haakon Sjursen , and  Ina Hjelland  for their help with developing and testing the software. We gratefully acknowledge the  Norwegian, Chinese, German , and Turkish translations:  Kristian Bernhard Nilsen  (on behalf of The  Norwegian National Committee for Quality Assurance in Clinical Neurophysiology ),  Wu Xintong ,  Jie Mu ,  Friedhelm Schmitt , and  M.
TMS/tDCS-NF coupling is, however, confronted by two challenges: the first is algorithmic, the second is ethic. The algorithmic challenge involves determining the kind of brain activity that will be recorded and the kind of feedback that will be made, how all these data will be treated in real time and how to control unwanted effects. The first issue is related to the use of a new diagnostic system correlated to the neural network disturbance in mental disorders. The "Clinical Brain Profiling" advanced by Peled is an interesting approach to these novel therapeutic hypotheses based on TMS/tDCS-NF coupling  (Peled, 2006 (Peled, , 2009)). Peled proposed a new etiology-oriented diagnostic system for psychiatry based on neural network dynamics complexity and neural plasticity  (Peled, 2004). It provides an innovative heuristic for recording brain activity and will soon integrate data from TMS-EEG research  (Ilmoniemi et al.
Gene expression analysis Gene expression analysis was performed on 250 ng mRNA from ex vivo isolated Nrp1 -Tfh and Nrp1 + Tfh cells of 5 distinct tonsils. RNA extraction was performed using RNeasy micro kit (Qiagen). After cDNA synthesis (SuperScript TM III Reverse Transcriptase, Invitrogen), qPCR analysis was performed using custom designed qPCR plates (96 target genes, StellARray, Lonza) and SYBR Premix Ex Taq (Takara), in StepOnePlus Real-time PCR System (Applied Biosystems). Results were analyzed with Microsoft Excel as follows: gene expression (relative to ACTB) was calculated from Ct values with the 2 Ct(ACTB)-Ct(gene) formula for each sample independently. For every gene, differential expression in Nrp1 -and Nrp1 + Tfh cell populations was assessed using a paired student's t test. All gene expression data (Ct values, gene expression values, fold change and p-value) are compiled in Table  S1  available online.