dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Identification of a new distinct family of rhodopsins. Previously, we found a rhodopsin in Sphingomonas paucimobilis (SpaR) with a DTS motif  6, 8. To understand how many rhodopsins share similarities to SpaR, we bioinformatically retrieved all rhodopsin genes of bacteria from the UniParc database. We should note that the DTS motif was also found in rhodopsins of other clades, including bacterial: of the viral group-I rhodopsin present in Choano Virus, in PgV 7 and xenorhodopsin clade (e. g. , Anabaena sensory rhodopsin, ASR  [29] [30] [31]  ). This means that a the three-letter motif is insufficient to predict rhodopsin functions. Thus, we clustered them into subgroups based on their similarity to each other (blastp e-value threshold of 1e-65) and retrieved a clade of SpaR-like rhodopsins.
The pH-'mirror' behavior of SpaR is unusual in comparison with the known proteorhodopsins. Indeed, proteorhodopsins functioning is also pH dependent as they operate as outward proton pumps only at acidic pH. The protonation state of PR D97 (pKa D97 = 7. 68  3  ) is crucial for this variable vectorality. However, SpaR shows completely different pH dependence, operating only as an outwardly directed proton pump only at acidic pH and having no pumping activity under normal and alkaline conditions. Functional studies of SpaR proteoliposomes. We checked the assembly of the unilamellar structure of proteoliposomes consisting of soybean lecithin phospholipids by using the SAXS option of the Rigaku X-ray station (see Methods). The data (Supplementary Fig. 12a ) were fitted by the model of the unilamellar lipid vesicles (ULV) with a polydispersity, χ 2 = 1.
The crystals were red, rodshaped and reached ~60 μm in length. The structure of SpaR was solved at 2. 8 Å by X-ray crystallography (PDB ID: 8ANQ, Supplementary Data 1). Similarly to BR, SpaR forms trimers in the crystals (Supplementary Fig. 16 )  45. It is in line with the SEC and SAXS data described above. The protein protomers within the trimer are almost identical at the current resolution. Each protomer has seven transmembrane α-helices (A to G) connected with three cytoplasmic and three extracellular loops (Supplementary Fig. 17 ). Like in BR  46  , in SpaR the B-C loop forms a β-sheet, however it is shorter than that of BR. As in all known rhodopsins, the retinal co-factor is covalently bound to the K211 residue of the helix G. The retinal binding pocket of SpaR is similar to that of BR (Supplementary Fig.
5), 100 mM NaCl, 300 mM imidazole, and 0. 1% DDM. The eluate was concentrated by means of Stirred Cell (Amicon) with 30 kDa MWCO membrane. Then, the protein was additionally purified by size-exclusive chromatography using Superdex 200 Increase 10/300 GL column (GE Healthcare Life Sciences, USA). Finally, the fractions of the protein with a maximal peak ratio A 280 /A 540 were concentrated to 40 mg/ml for crystallization. Small-angle X-ray scattering (SAXS) measurements with solubilized. SpaR were performed in Grenoble (France), on the beamline BM-29 (synchrotron ESRF)  125. IFT-fits and Patterson functions P(r) were performed using the Gnom program from ATSAS software suite  126. An accurate SAXS data treatment for solubilized membrane proteins requires explicit accounting of the detergent belt contribution to the scattering profile  127, 128.
For this purpose, the MEMPROT program  33  was used to model the detergent corona around a protein and to fit the experimental SAXS data using this model. The sample of SpaR after gel-filtration was concentrated to ~5 mg/ml, and then, dialysis was performed for 12 h in a buffered solution 100 mM NaCl, 0. 5 mM EDTA, 0. 01% NaN 3 , 20 mM TRIS(HCl) pH7. 2, 0. 05% DDM. The SAXS profile is presented in Supplementary Fig. 6. For the trimer model, the optimal parameters of the detergent corona found by the fit are: a = 34. 5 Å, b = 5. 0 Å, t = 6. 5 Å, ε = 1. 19, φ = 79°(we used Adaptive shape algorithm type 2 (MBJP) and Crysol 3), that corresponds to the 400 detergent molecules around the SpaR trimer. Single lipid vesicles preparation for pump activity measurements were as described previously in ref.
The current-to-voltage converter of the PMT determines the time resolution of the measurement system of ca. 50 ns (measured as an apparent pulse width of the 5 ns laser pulse). Two digital oscilloscopes were used to record the traces of transient transmission changes in two overlapping time windows. The maximal digitizing rate was 10 ns per data point. The transient absorption changes were recorded from 10 ns after the laser pulses until full completion of phototransformation. At each wavelength, 25 laser pulses were averaged to improve the signal-to-noise ratio. The quasilogarithmic data compression reduced the initial number of the data points per trace (~50,000) to ~600 points evenly distributed in a log time scale, giving ~100 points per time decade. The recording of the absorption changes was started 700 ns after the laser pulse before the end of the photocycle, at wavelengths from 330 to 730 nm with a step of 10 nm.
The absorption spectra of the samples were measured before and after each experiment on a UV-2401PC spectrophotometer (Shimadzu, Japan). The obtained data were analysed by global fitting using the MEXFIT software  35. SpaR expression in NG108-15 cells. The eucariotic codon-optimized SpaR gene was also assembled by PCR with the use of 14 overlapping short synthetic oligonucleotides (Evrogen, Russia). The gene was cloned into the pcDNA3. 1(-) vector bearing an additional membrane trafficking signal from Kir2. 1 and an EYFP variant. The gene was cloned under the T7 promoter via KpnI and NotI restriction sites. The sequence was verified by sequencing. The NG108-15 cells were grown under 5% CO 2 at 37 °C in Dulbecco's Modified Eagle's Medium (DMEM) (Thermo Fisher Scientific, USA), containing 10% heat-inactivated fetum bovine serum, 1% penicillin/streptomiccin (Thermo Fisher Scientific, USA), 2 mM GlutaMax (Thermo Fisher Scientific, USA), and 10 mM HEPES pH 7.
4 (Thermo Fisher Scientific, USA). Cells were grown in 24-well plate (Corning Incorporated, USA) till confluency ~90%. Then they were transfected by Lipofectamine™ LTX Reagent supplied with PLUS™ Reagent (Invitrogen, USA) in accordance to the manufacturer's protocol (the amount of Lipofectamine™ LTX Reagent per well increased to 1,5 µL). After 4 h of incubation under 5% CO 2 at 37 °C, the transfection medium (Opti-MEM Reduced Serum Media, Thermo Fisher Scientific, USA) was changed back to the growth medium. After 12-24 h the cells were seeded on the glasses for further patch-clamp and microscopy experiments, which were conducted after 3-12 h. Whole-cell patch-clamp recordings on NG108-15 cells expressing SpaR were performed with Scientifica LASU, Axon Digidata 1550 A, Multiclamp 700B. Patch pipettes with resistances of 3 to 6 megohms were fabricated from thin-walled borosilicate glass (GB150F-8P) on a horizontal puller (Model P-1000, Sutter Instruments, USA).
Excitation of the SpaR-EYFP was carried out by 488 nm argone laser (Lasos, Jena, Germany). The resulting images were processed using the ZEN software (Zeiss, Germany) and the ImageJ, open-source image analysis and processing software (version 1. 52 u). Optogenetic experiments. Imaging was done using an inverted confocal LSM780 microscope (Carl Zeiss, Germany), 63x (NA = 1. 4, oil immersion) objective. An optical fiber (400 µm in diameter) guided through the microinjection needle holder was placed just above the cells of interest by means of microinjection micromanipulator InjectMan NI2 (Eppendorf). LED590 (ThorLabs M590F2, 590 nm emission maximum) was used with a power of 16 mW/mm 2 (ThorLabs Thermal Power Sensor Head S302C measured) in all experiments, excluding those with a LED power variation (ThorLabs 4-Channel LED Driver, DC4104).
During the experiments human cells HEK293T (ECACC 12022001) were placed in Tokai Hit CO 2 -incubator (model INUBG2H-ELY), HEPES (pH 7. 4) for the final concentration 25 mM was added, 50 nM Bafilomycin A1 (B1793, Sigma-Aldrich) was added 12 h before optogenetic lysosome acidification. For time series with light illumination λ-mode was used. Only pHluorin fluorescence intensity was excited by a 488 nm laser. The emission was measured in a CLSM λ-mode using a 34channel QUASAR detector (Carl Zeiss, Germany) set to a 488-545 nm range to avoid LED590 detection. Time curves were obtained using ZEN software by Zeiss after performing spectral linear unmixing processing. The time curves were averaged for different cells in the field of view. LED590 emission, which added intensity to the GFP spectrum, was obtained from the background ROI and then subtracted manually from time curves of lysosomes ROI to produce pure pHluorin fluorescence intensity time curves.
Estimation of protein denaturation temperature. Protein melting temperatures were determined as maxima of the first derivatives of the 330 nm SpaR intrinsic fluorescence during slow heating at the rate of 0. 5 °C per minute by the nanoDSF Prometheus Panta instrument (Nanotemper, Germany). Purified (>98%) protein at concentration of 0. 5 mg/ml was used in 20 mM Tris(HCl) pH5. 5 or pH7. 0, 150 mM NaCl, 0. 05% DMM, 0 or 10 mM ZnCl 2 (Sigma-Aldrich, #229997). The data analysis and graphs plotting were performed by means of Panta Analysis software (Nanotemper, Germany). Crystallization. The crystals of SpaR were grown with an in meso approach  43  , similar to that used in our previous works  143, 144. Namely, the solubilized protein (40 mg/ml) in the crystallization buffer was mixed with premelted at 42 °C monoolein (MO, Nu-Chek Prep) in a 3:2 ratio (lipid:protein) to form a lipidic mesophase.
Before freezing, harvested crystals were incubated for 5 min in the respective precipitant solutions. Crystals were then harvested using micromounts (Mitegen, USA), flash-cooled and stored in liquid nitrogen. Diffraction data collection and treatment. X-ray diffraction data were collected at the ID29 beamline of ESRF (Grenoble, France) using a PILATUS 6M-F detector. Diffraction images were processed using XDS  145. There is no possibility of twinning for the crystals. The data treatment statistics are presented in the Supplementary Table  1. Structure determination and refinement. Initial phases for the ground state of SpaR were successfully obtained in the C2 space group by molecular replacement using MOLREP  146  from the CCP4 program suite 147 using the 1C3W structure of BR rhodopsin  148  as a search model.
Acknowledgements The authors acknowledge  Nina Maliar  for her generous help with the experiments and  Dr. Alexander N. Popov  for the great help at the  ESRF ID23-1  and ID30b beam stations. The authors thank the  Head of Foreign Languages Department of Moscow Institute of Physics and Technology , associate professor  Dr. Elena Bazanova  for editing the manuscript. We acknowledge the  Structural Biology Group  of the  European Synchrotron Radiation Facility  for granting access to the synchrotron beamlines. The work used the platforms of the  Grenoble Instruct Centre (ISBG ;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB). The work was done in the framework of  CEA(IBS)-HGF (FZJ) STC 5.
As we discuss later, attention was considered in our model by changing the amount of information that could be selected (more or fewer values in the database), varying iterative learning by modulating the number of epochs  36  , and varying memory by modulating the number of formal neurons embedded in the hidden layer  37. In summary, in our computational modeling approach, we created three versions of the same model allowing us to simulate participants' data in a reproduction task. First, we tested a Simple Recurrent Neural Network (RNN) in order to replicate the Recurrent Neural Network model used in the judgment  18, 20. Second, we launched this model multiple times by varying each of the four parameters described above: (1) clock plasticity; (2) attention; (3) temporal learning by iteration; and (4) memory, one by one, in turn.
For each simulation we calculated the temporal dispersion coefficient, which was the slope calculated using a linear regression associated with the cumulated Euclidean distance of the error generated by each of the models. Third, we gathered all these temporal dispersion coefficients into a second model, which was a MLP (Multilayer Perceptron) that could hold all the errors of the RNN associated with the different modulations of the four parameters. Fourth, we created a third and final version of our model in order to compare the outputs of that final model with the data collected from 308 human participants in a temporal reproduction task (both children and adults), whose individual cognitive capacities were assessed using neuropsychological tests. The final model used real individual neuropsychological test scores and age (inputs) to compute the state of each of the four parameters of our clock system, in order to predict individual errors in the temporal estimation for a temporal reproduction task (outputs).
(  1 ): where σ is the sigmoid activation function applied coordinate wise, while A and R refer to the weight between each of the units. Finally, the output layer (y t ) sums the signals fed to it according to Eq. (  2 ): U is the weight assigned by the program for each of the units constituting the hidden layer, and f the soft-max function which converts a vector of real numbers into a probability distribution of possible outcomes. Nevertheless, the system does not find the weight of each of the neurons at one time. Calculation results from so-called gradient retro-propagation, which consists of updating the weight of each neuron from the last layer to the first  38. It is therefore necessary to fix the number of allowed epochs. An epoch describes the number of times (1) h t = σ (Ax t + Rh t-1 ) (2) y t = f (Uh t ) the algorithm sees the entire dataset.
So, each time the algorithm has seen all samples in the dataset, an epoch is completed. Once the retro-propagation of the gradient is complete based on the number of epochs, we can then calculate the error between the output given by the network and the predicted vector of the output. Within our RNN, a total of 12 formal oscillatory neurons were inserted as input neurons, each one corresponding to a neuron fluctuating from negative to positive states every t millisecond. The respective values of the oscillators (see Fig. 2 ) were (Hz): F o1 = 32. 9, F o2 = 33. , F o12 = 37. 2. These Hertz values were selected for their similarity to the pattern of neural gamma oscillations observed in humans  39  , which have been detected in the process of awakening  40. In addition, in our model, such as in the SBF model, the selected neurons for the measurement of time were  synchronized at the beginning of a stimulus to be timed.
Plastic-clock and cognitive components in the RNN clock model. The aim of our model was to grasp the greater time distortion and the greater temporal variability in participants with lower cognitive capacities (including children of different ages and adults). We therefore modulated a total of four components: plasticity, temporal learning by iteration, memory, and attention. With regard to attention, when participants have difficulty paying attention to time, their time estimates are noisier  6, 7, 42. In our model, the attention parameter was located in the input layer, X t and attention deficit results in a decrease in the amount of information presented in the database relating to the evolution of oscillators' states according to the progress of t (as is illustrated in Fig. 4  by black squares).
The system can therefore trace the evolution of oscillations up to 1024, 1792, 2560, 3328 or 4096 ms. The memory parameter was located in the hidden layers, H t. We acted on the memory system by increasing or decreasing the retention capacity via changing the number of formal neurons within the hidden layers. Indeed, increasing the number of neurons increases the model's capacity to remember what has previously been presented in the database  37. We thus chose a varied number of neurons, i. e. , 8, 16, 32, 64, and 128, in the hidden layer in order to model this variable. Furthermore, as was explained above, a plastic system leads to faster learning but also to further forgetting, thus corresponding to the loss by the subject of the state of oscillations. As plasticity is associated with forgetting, it occurs during the computation as illustrated in Fig.
Finally, the subject can give an estimate which corresponds to the cumulative average of the Euclidean distance averages of the objective-subjective differences. Nevertheless, in the case of iterative temporal learning, the whole operation will be restarted. Further, predictions will increasingly suffer from the weight of the prior estimation, which will scramble the initial standard duration, thus increasing the proportion of noise in the reference memory. parameter involved in temporal reference was modulated by modifying the number of epochs  36. As previously stated, an epoch corresponds to the number of times the algorithm sees the complete dataset. This factor has been linked with iterative learning, as less learning solicits less feedback and thereby fewer epochs. The selected epochs were 100, 200, 300, 400, 500.
The model was launched 875 times, modulating each parameter one by one (5 attention modalities × 5 memory modalities × 5 learning iteration × 7 plasticity = 875). Based on each of these computations the program predicted, with more or less error (e. g. , Euclidean distance), the states of each of the 12 oscillators from 1 to 4095 ms. Based on the cumulated Euclidean distance of this error through time, we launched a linear regression in order to generate an error slope that we called a temporal dispersion coefficient, associated with each of the computations (all ps < 0. 001; R 2 ≥ 0. 98). The link to OSF (section linear estimations) shows the matched dispersion coefficient with the different values of each parameter, i. e. , for different numbers of neurons (memory parameter), different plasticity values (Fahlman offset), different epochs (learning parameter) and different amounts of information presented in the input database (attention parameter).
66, p < 0. 001, η 2 p = 0. 10. Additional paired t-tests showed that conditions differed significantly from each other depending on the input data (the implemented database contained the values of the 12 oscillators up to 1024; 1792, 2560, 3328 or 4096 ms). As can be seen in Table  2 , errors seem to decrease as the database was provided. Yet, after a threshold of 2560 was reached, it appears that further implemented data does not help the system to work better (M 102 3 = 0. 196; M 1 79 2 = 0. 191; M 2 56 0 = 0. 201; M33 2 8 = 0. 203; M4 0 96 = 0. 203). This suggests a threshold effect of attention beyond which more attention does not have much influence. The ANOVA on learning-by-iteration parameter (Number of Epochs) also showed a main effect, F(4, 696) = 14. 18, p < 0. 001, η 2 p = 0. 07, and the last ANOVA also found a significant effect for the plasticity parameter (Fahlman offset), F(6, 744) = 597.
The architecture thus enables a feedforward mode of operation (as the context layer is no longer present). parameters were normalised on a [0; + 1] scale, with the minimum and maximum values being equal to 0 and 1, respectively and inserted as an input layer of a Multi-Layer Perceptron (MLP). Thereby, there were four input variables, namely the number of epochs (iterative learning parameter), the amount of information added to the database (attention parameter), the plasticity (plasticity parameter), and the number of neurons constituting the hidden layer (h t ) (memory parameter). There was also a fifth input variable, corresponding to bias. The bias is an additional parameter in Neural Networks which is used to adjust the output along with the weighted sum of the inputs to the neuron. We used the "Regressor" of the python's neural network toolbox named the "scikit-neural network" to represent this bias.
RNA integrity numbers (RIN) were above 7/8. RNA was processed and analyzed according to an adaptation of published methods  [50]. Briefly, cDNA was synthesized from 140 ng of total RNA for each structure by using qSript TM cDNA SuperMix (Quanta Biosciences). qPCR was performed with a LightCycler ® 480 Real-Time PCR System (Roche, Meylan, France). qPCR reactions were done in duplicate for each sample by using LightCycler 480 SYBR Green I Master (Roche) in a final volume of 10 µl. The qPCR data were exported and analyzed in an informatics tool (Gene Expression Analysis Software Environment) developed at the University of Bordeaux. The Genorm method was used to determine the reference gene  [50]. Relative expression analysis was normalized against two reference genes. Succinate dehydrogenase complex subunit (Sdha) and tubulin alpha 4a (Tuba4a) were used as reference genes for PFC.
Statistical analysis Behavioral and brain marker data were inspected for the identification of possible outliers using the Grubbs' test or the extreme studentized deviate ESD method. Identified outliers for a given variable were excluded from the statistical analysis. This explains the slight differences in the total number of animals per group among certain statistical comparisons. For each variable analyzed, the exact sample size is indicated in the corresponding figure or in its legend. Normality was assessed through the Shapiro-Wilks test for each experimental group and each variable of interest. Data from startle reactivity did not show a normal distribution at all stimulus intensities and were therefore subjected to natural logarithmic (ln) transformation in order to meet the normality requirements of ANOVA.
In contrast, in WT mice, the CBDV treatment at the dose of 100 mg/kg decreased the amount of affiliative behavior (Figure  4a , left). During the last bin of 3 min, no between group differences were observed, all comparisons being non-significant. Figure  4. Effects of subchronic adult CBDV treatment on social interaction and acoustic startle response (Study 1). Time spent performing direct affiliative behaviors (including sniffing and contact) towards an unfamiliar adult NMRI female mouse was assessed during the first and second 3-min time bins of a 6-min interaction session (a). Body startle (ln-transformed to meet the normality assumptions of parametric ANOVA) was measured in response to acoustic stimuli of 6, 12, 18 and 24 dB over a background of 66 dB (b). Data are expressed as mean ± SEM.
Acknowledgments: The authors thank  M. Maitre  and  H. Doat  of the  NeuroCentre Magendie Inserm U1215  for their work at the Laser Microdissection capture facility funded by  Inserm ,  LabEX BRAIN   ANR-10-LABX-43  and  FRM DGE20061007758. This work benefited from the support of the  Transcriptomic  facility funded by  Inserm  and  LabEX BRAIN   ANR-10-LABX-43 ; thanks to  T. Leste-Lasserre  and the personnel of the Transcriptomic platform of the  NeuroCentre Magendie Inserm  U1215. We thank  Delphine Gonzales  and the genotyping facility of Neurocentre Magendie, funded by  Inserm  and  LabEX BRAIN   ANR-10-LABEX-43 , for animal genotyping. The authors thank  Elodie Poinama  for the expert animal care,  Thierry Lafon  for technical assistance,  Marie-Laure Rousseau  for administrative support.