dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical analysis of full conversion data preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this this version posted February 9, 2022. ; https://doi. org/10. 1101/2022. 02. 09. 479090 doi: bioRxiv preprint 37 Solid CTZ was dissolved in ice-cold ethanol and stored under nitrogen atmosphere in dark glass vials at -20 °C. Before measurement, concentration and quality of the ethanol stock solution was verified spectrophotometrically. Series of buffer solutions with different azaCTZ concentration was prepared by manual injection of an appropriate volume of the ethanol stock solution into 10 ml of 100 mM phosphate buffer pH 7. 5 immediately before the measurement. The reaction mixture was composed of 10 % (v/v) enzyme solution in 100 mM phosphate buffer pH 7.
The recorded luminescence traces (rate vs. time) were transformed to reaction progress curves corresponding to cumulative luminescence in time. The transformed kinetic data (product vs. time) were fitted globally with the KinTek Explorer (KinTek Corporation, USA). The software allows for the input of a given kinetic model via a simple text description, and the program then derives the differential equations needed for numerical integration automatically. Numerical integration of rate equations searching a set of kinetic parameters that produce a minimum χ 2 value was performed using the Bulirsch-Stoer algorithm with adaptive step size, and nonlinear regression to fit data was based on the Levenberg-Marquardt method. 1  To account for fluctuations in experimental data, enzyme or substrate concentrations were slightly adjusted (± 5%) to derive best fits.
Binding of CEI was monitored by measuring fluorescence spectra ranging from 350 to 700 nm upon excitation at 330 nm. Excitation bandwidth was set to 5 nm, measurement time to 100 ms, and the temperature inside the instrument to 30 °C. The obtained fluorescence spectra were visually inspected for characteristic peaks, and the value of the maximal fluorescence intensity Fluomax at these peaks was plotted against the enzyme concentration. The final value of the dissociation constant Kd was determined by fitting the dependence with the hyperbolic Equation (1) using the software Origin 6. 1 (OriginLab, USA). 𝐹𝑙𝑢𝑜 𝑚𝑎𝑥 = 𝐹𝑙𝑢𝑜 𝑙𝑖𝑚 • [𝐸] 𝐾 𝑑 + [𝐸] + 𝐹𝑙𝑢𝑜 0 (1).
Diffraction data collection and data processing All X-ray data were collected at PXIII beamline at SLS Synchrotron (Villigen, CH) at the wavelength of 0. 999 Å using a Pilatus 2M-F detector. The data were processed using XDS 3 , and Aimless 4 was used for data merging. Initial phases were solved by molecular replacement using Phaser 5 implemented in Phenix 6 with apo-form AncFT (PDB ID: 6S97)  7  and RLuc8 (PDB ID:6YN2)  7  employed as search models. The refinement was carried out in cycles of automated refinement in phenix. refine program  8  and manual model building in Coot  9. The final models were validated using tools provided by Coot 9 and Molprobity  10. Visualizations of structural data were created using PyMOL  11. Structural superposition was carried out using the secondary structure matching (SSM) superimpose tool in the Coot  9.
The wavelength of the absorbance spectrum peak was plotted against the actual pH value, and the value of pKa was determined by fitting the dependence with the sigmoid equation (  2 ) where w represents the sigmoid width, m is the slope of a potential drift, and λlow and λhigh stand for limiting wavelengths of peaks at low and high pH, respectively. All the nonlinear fitting was performed using the software Origin 6. 1 (OriginLab, USA).
The simulations were performed using Easyspin  17  , a freely available package for EPR spectral simulations working for Matlab. 18 e reactions were prepared in glass vials with a final volume of 100 µL containing CTZ (685 µM) and DMPO (200 mM) diluted in dimethyl sulfoxide (DMSO). 40 µL of 50 mM phosphate buffer solution (PBS) pH 7. 5 were added to the reaction prior to EPR data acquisition. As a control, we measured a solution with the same content except for CTZ that was substituted by pure DMSO. The excess of DMPO was intended to avoid secondary radical formation in the reaction. The measured solution was pipetted into a quartz cuvette (flat cell) and measured at room temperature using a Magnettech X-band EPR spectrometer. The spectra were obtained with 10 mW microwave power and modulation of 0.
43 The X-ray structures of the azaCTZ-bound AncFT (PDB ID: 7QXR) and CEI-bound AncFT (PDB ID: 7QXQ) complexes solved in this study were used to model E. CTZ, E. 2-peroxy-CTZ, E. dioxetanone intermediate complexes. Then, PyMOL 11 was used to model the RLuc complex structures by superimposing the apo-form RLuc8 (PDB ID: 2PSF11; chain B)  19  and AncFT complexes, producing analogous RLuc8-ligand complexes for MD simulations.
Acknowledgements The authors would like to express their thanks to the  Czech Science Foundation  ( 22-09853S ) and the  Czech Ministry of Education  ( INBIO CZ. 02. 1. 01/0. 0/0. 0/16_026/0008451 , RECETOX RI LM2018121 , e-INFRA  LM2018140 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  ( TEAMING 857560  and Sinfonia  814418 ) and the  Marie Sklodowska-Curie Action  (No. 792772 ). M. M. acknowledges financial support from  GAMU of the Masaryk University  ( MUNI/H/1561/2018 ), and M. T. is a  Brno Ph. D. Talent Scholarship  holder funded by the  Brno City Municipality. CIISB research infrastructure project ( LM2018127 ) is acknowledged for financial support of the measurements at  Biomolecular Interactions and Crystallization Core Facility.
preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Corel Corporation, Canada) graphical software. The visual style for twodimensional drawings was adapted from PoseView (BioSolveIT, Germany) software. 13 bioRxiv preprint doi: ; https://doi. org/10. 1101/2022. 02. 09. 479090 this version posted February 9, 2022. The copyright holder for this adjusted in CorelDRAW X6 ( 40.
Reading GEVI fluorescence to steer optogenetic actuators with light feedback in closedloop An OVC should measure voltage-dependent fluorescence of an excitable cell, e. g. via a rhodopsin-GEVI  10, 11  , and provide adjusted light-feedback to optogenetic actuators of membrane voltage, e. g. cation-and anion-selective rhodopsin channels  19, 28, 33, 34  (Fig. 1A ). The OVC needs to work in closed-loop, to quickly counteract intrinsic activity. We first implemented suitable hardand software (Fig. 1B ; Supplementary Code 1): To excite fluorescence of rhodopsin GEVIs (e. g. QuasAr2, emitting in the far-red)  10, 11  , we expand a 637 nm laser to cover 0. 025 mm². GEVI fluorescence of a region of interest (ROI) is monitored by a camera, and compared to a target value (Fig. 1C , Supplementary Fig.
1A-C ). Light feedback is sent to the sample from a monochromator, whose wavelength limits can be pre-selected to match the chosen hyperpolarizing and depolarizing optogenetic tools' maximal activation, and that can adjust wavelength at 100 μs and 0. 1 nm temporal and spectral resolution, respectively. Communication between camera and monochromator is provided by a custom-written script in Beanshell (part of µManager interface  35  ), processing incoming grey values into relative changes of fluorescence (ΔF/F0) (Fig. 1C , Supplementary Fig. 1A-C , Supplementary Code 1). We initially performed step response experiments in open loop configuration and found the system to require barely 20 ms to reach the desired OVC step-input of ±5% ΔF/F0 (see below), which we conclude to approximate the system's time constant (Supplementary Fig.
Measuring all-optical I/V relationships The OVC allowed reliable optical voltage clamping. We wondered if we could also use it to acquire all-optical I/V-relationships. To this end, we devised a different software "pseudo I/V-curve" (Supplementary Fig. 8A ; Supplementary Code 1), which can consecutively present different F/F0 clamp steps. First, we performed optical experiments, relating different F/F0 clamp values (= voltage equivalent) to observed wavelengths (= current equivalent), required to achieve the respective fluorescence steps (Fig. 2F, G ). These purely optical experiments (in intact animals) showed that a range of at least ±10 % F/F0 can be achieved with wavelengths of ca. 470-585 nm, different to the simultaneous OVC/patch-clamp experiments (Fig. 2H ; Supplementary Fig. 8B ).
Optogenetic current-clamp and live-OVC Our approach enables to achieve an "optogenetic current-clamp". We wrote according software that allows to present continuous or pulsed wavelength ramps, or a single light pulse of the selected wavelength, while F/F0 is recorded live (Supplementary Fig. 9A ; Supplementary Code 1). This way, similar to earlier unidirectional approaches  11, 12  , we could induce and record APs (or inhibitory potentials) in BWMs in all-optical experiments (Supplementary Fig 9B  , C ). To further extend the applicability of the OVC, we wanted to enable the researcher to respond to observations and to dynamically adapt clamping parameters. We thus wrote software "on-therun" allowing to select holding ΔF/F0 values during a running acquisition (Supplementary Fig. 9D ; Supplementary Code 1).
150 ms (at full-width-half-maximum -FWHM)  20. To clamp these APs dynamically, i. e. , to counteract de-and repolarization phases quickly enough, we set the OVC software parameters to respond with more pronounced wavelength changes to a given fluorescence change (increased integral gain; methods, equation 6), and reduced the tolerance range to ± 0. 005 %. During calibration, APs showed 26. 0 ± 1. 6 % ΔF/F0 mean amplitude and 118. 7 ± 7. 3 ms duration at FWHM (Fig. 6B, C, E, F ). Upon clamping, all subsequent attempts to spike were dynamically counteracted and almost completely suppressed (Fig. 6B-F ). Clamping was rapid: APs occurring at clamping onset were shortened by 65 %, while the OVC traversed the full wavelength spectrum within 20 ms (Fig. 6D ). Signal amplitude was significantly reduced to ca.
Minimizing the optical crosstalk of voltage sensor and BiPOLES is especially important in mammalian neurons. Further red-shifted voltage sensors and blue-shifted BiPOLES variants are thus required for future OVC experiments. Light-driven ion pumps, provided they become as powerful as the channels used, could be a future alternative. The speed of the OVC, characterized by transition times of about 90-150 ms for the relevant induced F/F0 steps, is currently limited by the software that operates at a maximum sampling rate of 100 Hz. The software currently runs within µManager on a PC. Running the software on an integrated circuit, like a field programmable gate array, and using small ROIs, may enable faster sampling rates up to 1 kHz. The goal would be to identify the optimal sampling rate, thus bringing the transition time closer to the open loop system time constant of approx.
g. extent of body bending, locomotion velocity), may be used for feedback to the neuron such that behavior can be dynamically controlled. Transferring the OVC to other model organisms may require modifications depending on the respective cellular properties. Extending electrophysiology applications, and provided the monochromator light is projected via a digital micromirror device, the OVC should enable efficient space clamp, as well as dynamic local clamping in neuronal processes. The OVC software is universally applicable, as it can be adapted to other GEVI-optogenetic actuator combinations. The OVC broadens applicability of optogenetics as it allows modulation in closed-loop, to better adapt to the variable activity patterns found in living organisms. Dynamic responsiveness is also advantageous with regard to future therapeutic applications, for example in acute control of seizures  51  , or in adaptive deep brain stimulation, as it would allow adjusting the therapy to the need of the patient  52.
OVC software The OVC control software was written in Beanshell, the scripting language used by the open source microscopy software µManager v. 1. 4. 22  35. Scripts are provided in supplementary information (Supplementary Code 1). Experiments were initiated via the µManager script panel. For support of Polychrome V related commands, a copy of the Java archive file TILLPolychrome. jar must be placed into the MMPlugins folder and the dynamic link library file TILLPolychromeJ. dll into the Sys32 folder (both provided by Till Photonics). The main OVC software is compatible with all cameras supported by µManager. Before running the software, simple rectangular ROIs (to save computation capacity, Supplementary Fig. 1B ) must be selected in the live image mode by using the ROI button in the µManager main window.
Once a ROI is selected, the script can be executed via the script panel GUI. An input tab prompts the user to select the OVC-and acquisition parameters. The software allows to define a holding ΔF/F0 value (in %) and the number of frames for each of the steps (three-or four-step-protocol). One can further select the number of frames for the calibration period, the tolerance range (in %; range in which the actual value may fluctuate around the target ΔF/F0, and in which limits the control variable wavelength is set to hold), the algebraic sign of the increment factor that decides whether to increase or decrease the wavelength with respect to the current ΔF/F0, the starting wavelength and the wavelength limits (in nm). The free choice of these parameters ensures that the program can also be used for other and/or future combinations of optogenetic tools with different spectral properties.
In addition, "pseudo I/V curve" software (Supplementary Code 1) is available to clamp relative fluorescence in 11 consecutive steps based on previously selected upper and lower limits, resulting in an output of the average F/F0 value and associated wavelength achieved for each step (calculated as a mean of the last 25 % of each step). An optical wavelength / F/F0 curve is produced and can be translated into an estimated I/V-diagram using these linear calibration functions (Fig. 2J, L  Parameter tuning was performed by applying the Ziegler-Nichols method to empirically tune the P,I and D control gains for better control-performance (Supplementary Fig. 2C ). Therefore,   was adapted until the system started to oscillate at the critical proportional gain (   ). The oscillation period was noted as.
Correction of the covariance estimation ( + 1) = [ -( + 1)] * ( + 1|) (Eq. 17) The system matrices A and H were set as constant scalars (A = 1, H = 1; constant system dynamics) since this assumption is perfectly sufficient for signal smoothing for the PID controller. Here, the sampling rate is fast enough that the Kalman filter can respond to membrane potential changes occurring in this time period with reasonable estimates. Another software, "optical current clamp" (Supplementary Code 1) is used for the purely optical adaptation of a current clamp. The experimenter has the choice to set single pulses of certain wavelength and duration or to select step-like or continuous wavelength ramps prior to the experiment. Similar to the main OVC script (Supplementary Code 1), this software provides bleaching correction and live F/F0 readout.
00125% ascorbic acid, 0. 01 mg ml -1 insulin, 1. 44 mM CaCl2, 2 mM MgSO4 and 13 mM Dglucose. No antibiotics were added to the culture medium. Pre-warmed medium was replaced twice per week. SutterPatch software (Sutter Instrument). QuasAr2 was excited with a 637 nm red laser (OBIS FP 637LX, Coherent) at 0. 52 W/mm 2 via a dichroic mirror (660nm, Edmund Optics) and voltagedependent fluorescence was detected through the objective LUMPLFLN 60XW (Olympus) and through an emission filter (732/68 nm Bandpass filter, Edmund Optics). somBiPOLES was activated using a monochromator (Polychrome V, Till Photonics / Thermo Scientific), set to emit light from 400 to 600 nm at 1. 7 mW/mm 2. Laser and monochromator light were combined using a 605nm dichroic mirror (Edmund Optics). Irradiance was measured in the object plane with a PM400 power meter equipped with a S130C photodiode sensor (Thorlabs) and divided by the illuminated field of the Olympus LUMPLFLN 60XW objective (0.
Statistical Figures Figure 1 Components   (J, K) Statistical analysis of peak AP amplitude (wt / unc-13: p=8. 27E-4, wt / egl-19 p=3. 04E-4) and AP duration (at FWHM; wt / unc-13: p=0. 0082, wt / egl-19 p=1. 53E-6) of data in (G-I). n-numbers as in (G-I). (L) All-optical I/V-relationship (mean ± S. E. M. ) obtained in wild type (n=17) and egl-19(n2368) mutants (n=14) (2% p=0. 018, 3% p=9. 36E-9, 4% p=7. 26E-10, 5% p=7. 92E-9). (M) Data in (L) were transformed to I/V-relations (mean ± S. E. M. ), using calibrations in Fig. 2J , L. The currents were normalized using capacitance measured in patch-clamped muscle cells (analogous to Fig. 1I ). Data obtained for wild type (grey, n=17) was deduced from egl-19 data (yellow, n=14), generating a difference curve (dashed, yellow), and compared to difference data obtained by electrophysiology44 (n=16) in wild type and egl-19(n2368) mutants (blue).
, setup and functionality of the OVC. (A) Molecular OVC components: Two counteracting optogenetic actuators for de-and hyperpolarization (GtACR2, Chrimson; blue star: mCerulean), and a voltage indicator. (B) Hardware setup and communication: Membrane voltage is monitored via a fast and sensitive (sCMOS or EMCCD) camera; grey values are processed by live control software (Supplementary Code 1). Based on the difference to the set holding value, a feedback of light from a monochromator is sent to the optogenetic actuators. (C) OVC feedback mechanism: After selection of clamping parameters and calibration for bleaching correction, ΔF/F0 is calculated and compared with the set value, to adapt the wavelength accordingly, in closed-loop. (D) OVC (QuasAr2 and BiPOLES) in BWMs. (E) Confocal uorescence z-projection.
Acknowledgements We thank  Jiajie Shao  for support in confocal microscopy,  Adam Cohen  for reagents, and  Sven Plath ,  Regina Wagner ,  Hans-Werner Müller ,  Franziska Baumbach ,  Nico Sturman ,  Adrian Breicher ,  Timotheus Kozlowski ,  Rolf Bergs , and  Kathrin Sauter  for expert technical assistance. We are indebted to  Mathias Pasche ,  Wagner Steuer Costa ,  Achilleas Frangakis  and  Ernst Stelzer  for advice. We acknowledge funding by the following organizations: Goethe University (AG);  Deutsche Forschungsgemeinschaft ,  SFB807/B02  (AG),  SPP1926-XIb / WI 4485/3-2  (JSW), and  SFB1315/C01  (JV, PH);  Max Planck Society , PhD fellowship (ACFB);  Chan Zuckerberg Initiative  (QL, CIB).
Software. All reconstructions and analyses were carried out in Blender versions 2. 9 to 3. 1. 2 (http://blender. org). Reconstruction. The reconstructed Iniopera skeleton is a composite based on KUVP 22060 (skull, mandible, basihyal, basibranchial, and ceratohyal) and KUVP 158289 (shoulder girdle and ceratobranchial cartilages) (Fig. 1  and SI Appendix, Figs. S1  and S2 ). We imported models of all cartilages into Blender as separate. ply files. The components of the reconstruction are identical to those in figure 4 in ref. 29  with the exceptions that we corrected breaks in the scapulocoracoid and mandible that affected these elements' shapes, using Blender to realign and remesh the elements (SI Appendix, Fig. S1 E-L ). We reconstructed muscles based on the skeletal morphology of Iniopera using extant phylogenetic bracketing  (53)  with reference to the musculature in extant holocephalans and elasmobranchs  (13, 30).
S4C ), starting at three plausible resting positions at 3, 6, and 9 degrees from the maximum mandibular closure. Next, 3D cylinders were used to model the mandibular adductor muscle and were placed at the anteriormost and posteriormost ends of the estimated origin and insertion sites on the closed jaws (SI Appendix, Fig. S4B ). We parented these cylinders to single-bone armatures extending between the same two points, with their tail ends on the origin (SI Appendix, Fig. S4D ). These armatures were given stretch-to bone constraints targeting their point of contact with the mandible, meaning that the armature and cylinder stretched parallel to the axis of the jaw's rotation (SI Appendix, Fig. S4D ). We modified a Python script from Lautenschlager  (32)  that was used to calculate the extension of the muscle cylinders and output the strain factor for each frame.
Muscle cylinders were color-coded to correspond to within the optimal tension limit for mandibular adductor extension (green), within the maximum tension limit for mandibular adductor extension (yellow), and over the maximum tension limit (red). We captured renders of each frame to make figures. Ceratohyal Range of Motion. We estimated the range of motion of the ceratohyal in Iniopera relative to the basihyal in Blender. The articulation between the basihyal and ceratohyal was interpreted as a hinge joint, with one degree of freedom, based on its previously described anatomy  (29). This was represented with a cylinder, which was oriented with both articulation surfaces and the z axis of which was used as the joint axis  (54). The ceratohyal was animated to rotate around this axis in 0. 5-degree increments, and a Python script was written that detected overlap between the ceratohyal and basihyal meshes to determine the range of movement.
Mechanical Advantage and Estimated Muscle Force. We estimated the mechanical advantage of the jaw through the opening cycle using the 3D models of the neurocranium and mandible (Fig. 3  and SI Appendix, Fig. S5 ). The mandible was animated to rotate through to the estimated maximum possible extension of the mandibular adductor muscles over 160 frames. We added empties and parented them to models at the central positions of the mandibular adductor's origin and insertion, at the point of rotation for the articulation of mandible and neurocranium, and at three points (anterior, middle, and posterior) along the biting surface of the mandible (SI Appendix, Fig. S5A ). We wrote a Python script to output the coordinates of these empties in 3D space at each point in the animation and a script in R to calculate the vectors and lengths of levers involved in jaw closure.
Mechanical advantage was calculated with: Mechanical advantage = Inlever E Outlever : Muscle force was calculated using: F M = Specific tension × Area of muscle attachment: We interpret the mandibular adductor muscle in Iniopera as attaching to both the neurocranium and Meckelian cartilage without tendons as in living elasmobranchs (SI Appendix, SI text), and so took the area of muscle attachment as a proxy for the physiological cross-sectional area. We estimated this area using the surface area of the attachment surfaces on the 3D model, measured in Blender, and used the specific tension of elasmobranch skeletal muscle, namely, 28. 9 Ncm À2 (55). We then compared mechanical advantage and estimated muscle force with a dataset of posterior and anterior mechanical advantage and biting forces for extant chondrichthyans assembled by Motta and Huber (Table  6.
We wrote a Python script that exported the change in volume of the pharynx into a text file and rendered each frame of the animation. A likely inaccuracy of our model is that its movements are synchronized, unlike in living fishes where there is an anterior-toposterior wave of expansion  (35, 56) , although this should not impact our estimates of total volume change. Data Availability. All Python scripts, R scripts, and Blender files are available in the GitHub repository at the following link: https://github. com/rpdearden/Iniopera_suction. The original tomographic data for both specimens that the analysis is based on are available at Morphosource: specimen KUVP 22060 at http://n2t. net/ark:/87602/m4/ 478221  (57)  and specimen KUVP 158289 at https://github. com/rpdearden/Iniopera_ suction  (58).