dataesr/scientific-paragraphs-categorization huggingface dataset:
Immunostaining and quantification of cultured cells Immunostaining and microscopy of cultured cells have been described previously  (Dejanovic & Schwarz, 2014). Briefly, images were taken on a Nikon AZ-C2 + confocal laser scanning microscope as z-stack with three optical sections with 0. 5 lm step size. Maximum intensity projections were created and analyzed using NIS Elements 3. 2 (NIKON) software. Usually postsynaptic clusters of two 20 × 5 lm regions of interest per neurons were analyzed using the analyze particles option in NIS Elements. Only clusters between 0. 09 lm² and 1. 5 lm² were considered for analysis. Mean values were compared for significance using Student's t-test. Following antibodies were used for immunostaining: anti-gephyrin (1:50, clone 3B11, cell culture supernatant); anti-VGAT (1:500, 131003, Synaptic Systems).
Isothermal titration calorimetry Isothermal titration calorimetry (ITC) measurements were performed with recombinant 6His-tagged gephyrin or G375D and purified GlyR b-loop residues 378-426 (GlyR-bL). ITC experiments were performed at 25°C using a VP-ITC system with cell concentrations of 20-25 lM gephyrin and syringe concentrations of 200-350 lM purified GlyR-bL. The injection volume was 3-5 ll/ 3-5 s for each of 50 injections with 240 s spacing and an initial delay of 120 s. The syringe speed was 310 rpm and the reference power adjusted to 5 lcal/s. Raw data were analyzed with Origin 7 software.
Circular dichroism spectroscopy The far-UV circular dichroism (CD) spectra from 195-260 nm were recorded at 25°C on a J-715 CD spectropolarimeter (JASCO). Buffer of purified gephyrin was exchanged to 10 mM potassium phosphate, and samples were filtered before measurement. About 0. 2 mg/ml of gephyrin were used in a quartz cell of 0. 1 cm path length. Spectral acquisition was taken at a scan speed of 10 nm/min, 4 s integration time, and a bandwidth of 1 nm. An average of 10 scans was obtained for all spectra. Data were corrected for buffer contributions and smoothed using the software provided by the manufacturer (System Software version 1. 00).
Electrophysiology Hippocampal cultures from E19 Wistar rats were prepared and transfected on DIV9 using Effectene (Qiagen) as previously described  (Winkelmann et al, 2014). Rats were handled and killed according to the permit No. Y9002/15 given by the institutional ethics committee of the Max-Delbru ¨ck Center for Molecular Medicine (Berlin, Germany). Transfected neurons were recorded between DIV12 and DIV15 as described  (Forstera et al, 2010 ). An EPC-7 amplifier and Patchmaster software (HEKA) were used for patch clamp recordings and data acquisition. Patch pipettes, made from borosilicate glass (Science Products), had resistances of 2-6 MΩ when filled with the intracellular solution containing (in mM) CsCl (130), NaCl (5), CaCl 2 (0. 5), MgCl 2 (1), EGTA (5), and HEPES (30). The standard extracellular solution (pH 7.
4) contained (in mM) NaCl (140), KCl (5), MgCl 2 (1), CaCl 2 (2), HEPES-NaOH (10), and glucose (10). Cells were clamped at a potential of À50 mV. Series resistances (R s ), monitored by À5 mV voltage pulses (50 ms) applied every 5 s, were between 5 and 30 MΩ. Experiments with a more than 25% change in R s throughout the recording were discarded. Data were acquired with a sampling rate of 10 kHz after filtering at 2. 8 kHz. Transfected cells were identified according to EGFP fluorescence. GABAergic mIPSCs were recorded in the presence of tetrodotoxin (TTX, 1 lM; Sigma), isolated pharmacologically by blocking NMDA receptors with DL-aminophosphonovaleric acid (APV, 50 lm; Sigma), AMPA receptors with 6,7-dinitroquinoxaline-2,3-dione (DNQX, 10 lM, Sigma), and glycine receptors with strychnine (1 lM; Sigma).
Quantitative analysis of mIPSC parameters was performed with an in-house software written in IGOR 6. 37A (WaveMetrics) by M. Semtner. Traces of current profiles from 17 different recorded neurons (1 to 5 min long) were analyzed for each condition. The first 50 mIPSCs from an analyzed trace were taken for statistical analysis. Average amplitudes from the experiments were taken for statistics, summarized as mean AE SEM for each condition. The frequency of mIPSCs was determined for each measured cell by dividing the number of events (50) by the analyzed time interval that had passed until 50 events were recorded.
Acknowledgements We thank the patients and their families for their cooperation to this study. We thank  Joana Stegemann ,  Monika Laurien  and  Veronika Georgieva  for technical assistance,  Belaidi Abdel Ali  and  Sita Arjune  for help with the MoCo assay,  Ines Neundorf  for peptide synthesis, and  Marcus Semtner  and  Aline Winkelmann  for their experimental support of the neuronal culture electrophysiology. T. D. is a PhD fellow of the  Institute of Science and Technology (IWT). A. S. is a postdoctoral fellow of the  Research Foundation-Flanders (FWO). This work was supported by the  German Science Foundation  ( DFG SFB635  to G. S. ), the program " Investissements d'avenir "  ANR-10-IAIHU-06 ,  Wellcome Trust  grants  089062  and  098051  (A. P. ),  European Commission   Framework Programme 7 (FP7 ) projects Synsys-242167 and gEUVADIS-261123 (A.
P. ),  Academy of Finland  grants  251704  and  263401  (A. P. ), the  Sigrid Juselius Foundation  (A. P. ), the  US NIH  grant  HL113315  (A. P. ), the  Eurocores program EuroEPINOMICS of the European Science Foundation (ESF) , the  Fund for Scientific Research Flanders (FWO)  (P. D. J. ), and the  University of Antwerp  (P. D. J. ), the  French program  " Investissements d'avenir"  ( ANR-10-IAIHU-06 ) (S. W. ), the  Bundesministerium für Bildung und Forschung BMBF (Era-Net NEURON II CIPRESS  to J. C. M. ), and the  Deutsche Forschungsgemeinschaft DFG   Priority Programme SPP 1784  ( ME2075/7-1  to J. C. M. ).
Experimental arena with controlled odor gradient A controlled odorant environment was created in a 120 × 120 × 12 mm arena consisting of a polystyrene dish (the lid of a Greiner square dish ref. number: 688102, Sigma-Aldrich, St. Louis, MO) standing on a 2% wt/V agarose surface inside the closed-loop tracker. A 3-μl odor droplet of IAA (0. 25 M) was placed inside a plastic reinforcement ring at the center of the dish (internal diameter of disk occupied by the odor droplet: 5 mm). Inside the arena, an odor gradient emerged as a result of the diffusion from the source for 30 s prior to the introduction of a single larva. This step required a brief opening of the arena. The tracking was carried out for a minimum duration of 3 min. A minority of trajectories associated with no chemotactic response or with larvae idly dwelling under the odor source was excluded from the dataset.
Behavioral quantification Run/non-run classifiers To detect turning events, we adopted a geometrical approach based on the physical trajectory described by the larva. First, we parsed the trajectory into segments of equal sizes. Next, we calculated the angle between successive segments of the trajectory. Finally, we computed the distribution over these angles. This distribution had the characteristics of a long-tailed exponential (data not shown). We defined a threshold at the location of the 'kink' of the distribution. Turning events were associated with positions with a turning angle larger than the threshold. We empirically found that good results were obtained for trajectory segments of 5 mm and an angular threshold of 20˚. The results of the behavioral classification were insensitive to the precise length of the trajectory segments and the angular threshold.
Since the odor gradient was initially established in the arena for 30 s prior to the introduction of a larva, our model also included non-zero initial concentration of the odor in the air, agarose, and plastic chamber. COMSOL Multiphysics v4. 3 (COMSOL, Burlington, MA) was used to solve the diffusion equation with these boundary conditions. Parameter estimation was performed using the MATLAB/Optimization toolbox (MathWorks, Natick, MA) by solving a nonlinear least squares problem that matched the simulated odor concentration to measurements at the same time points. The geometry of the experimental arena is described in Figure  9. The radius r ring of the odor ring confines the liquid droplet so that the radius of the flat face is equal to r ring. The volume V drop of the droplet, made up of odor and solvent, is fixed to be 3 μl.
The extracellular signal was amplified 100 times at the microelectrode amplifier; it was visualized on an oscilloscope (Tektronix, Beaverton, OR), and recorded at a sample rate of 20 kHz by a personal computer (PC) equipped with the free data acquisition software SpikeHound  (Lott et al. , 2009). The conception of this preparation and recording technique benefited from pioneering recordings from the larval olfactory organ  (Oppliger et al. , 2000; Kreher et al. , 2005; Hoare et al. , 2008).
Light stimulation at electrophysiology rig For the light stimulation, a blue light emitting diode (LED) (LCS-0470-03-22 LED, Mightex, Canada) was mounted in a lighthouse (U-DULHA, Olympus, Japan) and integrated into the light path of the microscope (BX51, Olympus) allowing for localized stimulation of the larval head through a 40× immersion objective. The light intensity arriving at the larval head was estimated by measuring the photocurrent under the objective with a photodiode (SM05PD7A, Thorlabs, Newton, NJ) connected to a bench top photodiode amplifier (PDA200C, Thorlabs). The LED was controlled by a custom Labview (National Instruments, Austin, TX) interface available from the following link: https://github. com/LabLouis/eLife_2015/tree/master/Electrophysiology. The current controlling the LED was fed into the data acquisition software, where it was recorded along with the signal from the suction electrode.
Spike-sorting algorithm Spike-sorting and PSTH analysis was performed with Matlab using custom scripts available from the following link: https://github. com/pahammad/OpSIN. In the Orco null background, spontaneous activity of OSNs is drastically reduced but not abolished  (Hoare et al. , 2008). Furthermore, non-OSNs contribute to the activity monitored from the DO ganglion. We devised an optogenetic spike-sorting strategy to distinguish spikes of the Or42a OSN from other spikes (Figure  11B, C )  (Lima et al. , 2009). Channelrhodopsin-2 (ChR2-H134R) was expressed in the single functional Or42a OSN (Or42a>ChR2). The spike-sorting algorithm (called OpSIN) parsed the extracellular recording data and collected candidate ChR2-evoked spikes specifically during light-activation time windows based on amplitude k plastic Robin rate for air-plastic boundary 5.
The output pressure and thereby the flow of each individual channel of the odor delivery pipette was controlled at a temporal resolution of 10 Hz via a custom Labview interface available from the following link: https://github. com/LabLouis/eLife_2015/tree/master/Electrophysiology. Rapid concentration changes of the odorous stream were achieved by varying the flow between the empty channel and the odor channel while keeping the overall flow constant at a set value of 0. 32 nl/s (measured flow rate corresponding to a 320 mbar output pressure). The experimental setup is outlined in Figure  12A. The liquid phase odor stimulation system was calibrated using a dilution series of the fluorescent dye fluorescein, which was expelled at a constant pressure (320 mbar) into the perfusion chamber (Bioscience Tools, San Diego, CA) while imaging the tip of the pipette with a high-speed camera (Andor, United Kingdom).
Phase conversion for olfactory stimulation Gas-phase odor stimulation was achieved by means of a custom olfactometer delivering a continuous stream of air (510 ml/min). The air stream, regulated by two mass flow controllers (Cole-Parmer, Vernon Hills, IL), was humidified and subsequently passed through the odor solution and delivered to the tip of the larval head. Rapid concentration changes of the odorized stream were achieved by varying the flow between the empty channel and the odor channel while keeping the overall airflow constant. A custom Labview protocol was used to control the flow rate of the individual mass flow controllers at a rate of 30 Hz. This script is available from the following link: https://github. com/ LabLouis/eLife_2015/tree/master/Electrophysiology. The experimental setup is outlined in Figure  12B : it is similar in design to past olfactometers  (Borst and Heisenberg, 1982; Kim et al.
The result of the NM optimization was then refined through a gradient search algorithm  (Brun and Rademakers, 1997). For the dataset corresponding to the light stimulation, the fitting procedure led us to rule out the relevance of the IFB model alone with a probability of χ 2 very close to zero. In contrast, the IFF was able to reproduce the experimental observation with good accuracy. On the other hand, in the case of the odor stimulation, we obtained a significant improvement of the model fit by adding an IFB component to the IFF motif (addition of the term α 3 y to the dynamics of u). The fitted value of the composite IFF+IFB motif indicated that the IFB component was not negligible during the stimulation and accounted for about 30% of the final firing rate (Figure  4D ). In contrast to the pure IFF model, variables u and y of the IFF+IFB model were entangled in the structure of the ODE resulting in a coupling that allowed us to fit the value of parameter α 1.
Tracker The tracker outlined in Figure  5  is presented in more detail in Figure  15. It was conceived and built at the Instrumentation Design and Fabrication Facility at the Janelia Research Campus. Part of the construction was carried out by KeyTech (Baltimore, USA). Unlike other tracking systems  (Faumont et al. , 2011; Leifer et al. , 2011; Kocabas et al. , 2012) , our setup was designed to keep the stage on which the larva evolves fixed by mounting the camera and stimulation LEDs on a moving stage whose position was continuously updated to stay locked with the animal's position (Figure  15 ). The upper and lower moving stage were powered by a pair of stepper motors (T-LSR450B, Zaber Technologies, Canada). The blueprint of the tracker and list of parts are available from the following link: https://github.
This C program enabled the LTCU to respond to the commands issued by the PC. The software interface of the tracking and image analysis software interfacing the LTCU was written in JAVA. Both the C program and JAVA interface are available from the following links: https://github. com/LabLouis/eLife_2015/ tree/master/Wormsign (tracker) and https://github. com/LabLouis/eLife_2015/tree/master/Venkman (JAVA controller interface).
1 mm for the family of light landscapes (Figure  7 ). The minority of trajectories from which larvae failed to detect the presence of the light gradient was discarded from the dataset. For open-loop experiments described in Figure  5 , the LED intensity was updated based on a predefined temporal function. This function was only implemented when the larva was classified in a run mode according to the rules described in Figure  16. Throughout the study, odor and light gradient landscapes were represented using brewermap (S. Cobeldick, MathWorks file exchange) with color schemes from http://colorbrewer2. org/.
Computation and predictions of the turn probability In the behavioral experiments of Figure  5 , the probability of turning ('turn rate') was estimated from the relative number of turns that took place during a 1-s time window centered on the time point of interest. If we denote the number of runs observed at the beginning and the end of the ith time window as Nb i and Ne i , respectively, Nb1 represents the total number of runs contained in the dataset. Over time, Nb i decreases monotonically to 0. The turn probability associated with the ith time point was estimated as the fraction of runs that ended during the corresponding time window: (Nb i -Ne i )/Nb i. A sliding time window was then applied to estimate the turn probability corresponding to every time point of the experiment. The turn probability was thus defined as the likelihood of implementing a turn within a 1 s time window, resulting in values smaller than 1 (in the case of 1 all runs entering a given time window switch to a turn) and larger or equal to 0 (in the case of 0 no turn takes place during a given time window).
Acknowledgements We thank  A Leonardo ,  E Knoche , and the  Louis lab  for comments on the manuscript. The tracker was developed with the help of  L Ramaswamy  (ID&F at  Janelia Research Campus). We are grateful to the following individuals for insightful discussions at different stages of the project:  C Bargmann  (molecular interpretation of the IFF motif),  H Davidowitz  and  D Rinberg  (design of olfactometer in gaseous phase),  A Kim  and  A Lazar  (comparison between transfer function adult and larval OSNs),  A Leonardo  (linear filter analysis and data interpretation),  R Moreno  (likelihood analysis),  M Phillips  (turn classifiers). We thank  S Pulver  and  L Griffith  for donating the UAS-ChR2-H134R transgenic stock. ML acknowledges funding from the  Spanish Ministry of Science and Innovation (MICINN ,  BFU2008-00362 ,  BFU2009-07757-E/BMC  and  BFU2011-26208 ).
We are grateful to  KeyTech (Baltimore, USA)  for advising about the design of the tracker and for carrying out the assembly of the hardware. AS, AGM, VGR, MM, JS, JR, DJ, and ML were supported by the  EMBL/CRG Systems Biology Program. JS was supported by  ICREA. AD was funded by the  European Commission   FP7 Initial Training Network FLiACT  ( 289941 ). MS was partially funded by the  National Centre for Biological Sciences-Tata Institute of Fundamental Research. GL, PA, ETT, CW, SD, and VJ were supported by the  Howard Hughes Medical Institute. This project received support from the  HHMI Janelia Research Campus visitor program  (AS, AGM, VGR, DJ, and ML). Ajinkya Deogade Tata Insitute of Fundamental research (TIFR) (NCBS) Madhusudhan Venkadesan Howard Hughes Medical Institute (HHMI) Janelia visitor programme Matthieu Louis Spanish Ministry of Science and Innovation   BFU2011-26208   Aljoscha Schulze ,  Alex Gomez-Marin ,  Vani G Rajendran ,  Julia Riedl ,  David Jarriault ,  Matthieu Louis Spanish Ministry of Science and Innovation   BFU2009-07757-E/BMC Aljoscha Schulze ,  Alex Gomez-Marin ,  Vani G Rajendran ,  Julia Riedl ,  David Jarriault ,  Matthieu Louis The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Funder Grant reference experimental data), Tracker development (machine vision algorithm), Drafting or revising the article; VGR, Conception and design (part of the behavioral experiments),  Acquisition of data  (early dataset for behavior), Tracker development (behavioral classifiers); GL, Tracker development (machine vision algorithm), Conception and design; MM, Tracker development (machine vision algorithm), Conception and design, Analysis and interpretation of data; PA, Spike-sorting method based on optogenetics (electrophysiology), Conception and design, Analysis and interpretation of data; AD, Conception and design (analysis of model predictions); JS, Design of Simplex parameter optimization (modeling), Drafting or revising the article; JR, Development of the OSN recording technique, Conception and design, Contributed unpublished essential data or reagents; DJ, Development of the OSN recording technique and spike-sorting algorithm, Conception and design, Analysis and interpretation of data, Contributed unpublished essential data or reagents; ETT, Tracker development (controller algorithm), Conception and design; CW, Tracker development (hardware), Conception and design; MV, Conception of model and analysis of data related to the PDE simulations of odor diffusion (modeling), Acquisition of data, Analysis and interpretation of data; SD, Conception of generalized linear model for behavior (modeling), Conception and design, Analysis and interpretation of data, Drafting or revising the article; VJ, Conception and design of the experiments, Spike-sorting method based on optogenetics (electrophysiology), Analysis and interpretation of data (electrophysiology), Drafting or revising the article; ML, Conception and design of the experiments and models, Tracker development; Analysis and interpretation of data (electrophysiology, behavior and modeling), Drafting or revising the article.
These data indicate a long-standing activity of these two elements in Bgh genome, during which a high diversity of degenerate copies has been generated. New effector genes may have evolved from these degenerated copies in a recycling and neofunctionalization process driven by the coevolution between Bgh and its host plant (Fig. 6 ). We have found 293 truncated copies of Satine and Kryze ORF1s that may be AVR/effector candidates (Table  3 ) and peptides that indicate translation of these genes (Table  4 ). These candidates have arisen several times during the evolution of the EKA family (Fig. 4 ), and belong to different lineages that could be related with the adaptation of B. graminis to different hosts.
TE de novo detection and annotation in Bgh genome The TEdenovo pipeline  [19, 20]  from the REPET package [https://urgi. versailles. inra. fr/Tools/REPET] was launched on the BluGen Bgh assembly of isolate DH14 (BluGen, Blumeria Genome Sequencing Consortium, http:// www. blugen. org/) to detect and classify 2251 TE consensus sequences. The TEdenovo pipeline detects TE copies, groups them into families and defines the consensus sequence for each family containing at least three copies. These TE consensus sequences are classified according to their structural features and similarities with known TEs. In addition to Repbase update v16. 03  [26]  used at the TEdenovo pipeline similarity search step to classify TE consensus sequences, the Bgt_RepeatLibrary TE library  [22]  and a Bgh_TE library (unpublished) including Satine and two already characterized Bgh class I -SINE retroelements (Bgh_EGR1_cons and Bgh_EGH24_cons)  [24, 25]  were included.
A first TEannot pipeline  [21]  from the REPET package was launched to annotate TE copies in the genome. A copy is considered as a full-length copy if the assembly of all its fragments represents 95 to 105 % of the TE consensus length. This first TEannot pipeline found full-length copies for at least 1465 consensus sequences (hereafter named Blgr_refTEs_FL library) that were used to launch a second TEannot pipeline. In order to refine TE annotation, Blgr_refTEs_FL was manually curated by curating or deleting potential chimeric consensus sequences and consensus sequences shorter than 0. 4 Kb. A library of 733 TE consensus sequences (hereafter named Blgr_refTEs) was finally obtained, including Satine and the two already characterized Bgh class I -SINE retroelements Eg-R1_cons and Egh24  [24, 25] , and was used to annotate the genome TE content.
Search for AVR/effector candidates We used blast (tblastn)  [27]  to search AVRK1 and AVRA10 peptide sequences against in-house databases of copies of TE consensus sequences similar to AVRK1 and AVRA10 (e < = 1e-05, length of hit > 50 amino acids). We discarded the shorter sequences (length < 75 % of AVRK1/AVRA10 length) and aligned the remaining sequences and AVRK1 or AVRA10 using muscle  [59] , identified the 63 amino acids that represent AVRK1/AVRA10 core sequence  [4]  and discarded the aligned sequences with a shorter core sequence (<75 % AVRK1/AVRA10 core). We also discarded the sequences with a stop codon in the region aligned with AVRK1 or AVRA10. The resulting sequences were used to compile a database of predicted 415 EKA proteins (Table  3 ). We identified the NB domain in the remaining sequences using fuzzpro  [60]  and classified the sequences as full-length or truncated depending on the position of the stop codons.