dataesr/scientific-paragraphs-categorization huggingface dataset:
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/cells12131752/s1, Figure  S1 : Ribonuclease and nuclease activities of the New Delhi Metallo-β-lactamase 1 (NDM-1). (A) bacterial RNA substrate hydrolysed by the NDM-1 enzyme in the presence and absence of inhibitor (i. e. , sulbactam and EDTA). (B) Single strand bacterial DNA (ssDNA forward and reverse) hydrolysed by the NDM-1 enzyme while double strand DNA (dsDNA) was not hydrolysed. H 2 O, Blank, and GO (Glycine oxidase) have been used as negative controls, and bacterial DNase has been used as positive control. The methodology and material used for this experiment have been reported in our previous work  [30]. Table  S1 : BlastP result of the MβL fold ancestor (120 aa) sequence against the Asgard group database (NCBI) (94 hits); Table  S2 : BlastP results of the MβL fold ancestor (120 aa) against the Nanoarchaeota database (NCBI) (40 hits).
6-Acknowledgements This work was performed using HPC resources from GENCI-A0090911942 and CALMIP-Grant P1418. The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 871813 MUNDFAB. This work was partly supported by LAAS-CNRS micro and nanotechnologies platform member of the French RENATECH network.
Flux data measurements A closed path EC system (CPEC200, Campbell Scientific Inc. , UT, USA) was applied to monitor the exchange of latent heat (LE) and sensible heat flux (H) between the land surface and the atmosphere. The EC system consisted primarily of a closed path CO 2 /H 2 O gas analyzer (EC155, Campbell Scientific Inc. , USA) for measuring CO 2 and water vapor concentration, and a three-dimensional sonic anemometer (CSAT3A, Campbell Scientific Inc. , UT, USA) for collecting wind velocities, temperature and humidity meter (HMP45C). Data was recorded at 10 Hz using a data collector (CR6 Campbell Scientific Inc. , UT, USA). The original 10 Hz flux data was then processed by standard routines, including 2-D coordinate rotation correction, time lags compensation, and frequency response correction  (Falge et al.
, 2022b). Despite the significant potential advantage of the P-M type model in ET and its component estimation, the primary challenge in practical usage was accurately addressing the surface resistance  (Ortega-Farias et al. , 2010) , since it is the important regulator during the heat and vapor transfer process, therefore, uncertainty in resistance parameterization can propagate in ET estimates, particularly when physical boundaries of resistance are required for the parameterization in regions without observations  (Liu et al. , 2020b). As far as the parameterization of the Jarvis model in this study, we only conducted the local calibration in the PM model, but without any calibration in other P-M type models for the convenience of sensitivity analysis. If these resistances were calibrated with observed datasets in each model, uncertainty arising from the resistance estimates may be reduced, which could be a possible solution for reducing simulated error  (Ortega-Farias et al.
Acknowledgments This work was also financially funded by the  National Key Research and Development Program of China  ( 2021YFD1600803-1 ),  National Natural Science Foundation of China  ( 52279041 ),  Fundamental Research Funds for the Central Universities  ( YJ202259 ,  2020CDDZ-19 ),  Science and Technology Department of Tibet  ( 2022wz002 ), and the  Sichuan Science and Technology Program  ( 2023YFN0024 ,  2023NZZJ0015 ,  2022YFQ0028 ).
Context. The ability to accurately predict the contrast achieved with high-contrast imagers is important for efficient scheduling and quality control measures in modern observatories. Aims. We aim to consistently predict and measure the raw contrast achieved by SPHERE/IRDIS on a frame-by-frame basis in order to improve the efficiency of SPHERE at the Very Large Telescope (VLT) and maximise scientific yield. Methods. Contrast curves were calculated for over 5 yr of archival data obtained using the most common SPHERE/IRDIS coronagraphic mode in the H2/H3 dual-band filter. These data consist of approximately 80 000 individual frames, which were merged and interpolated with atmospheric data to create a large database of contrast curves with associated features. An empirical power-law model for contrast -motivated by physical considerations -was then trained and finally tested on an out-of-sample test dataset.
Results. At an angular separation of 300 mas, the contrast model achieved a mean (out-of-sample) test error of 0. 13 magnitude with the 5th and 95th percentiles of the residuals equal to -0. 23 and 0. 64 magnitude respectively. The models test-set root mean square error (RMSE) between 250 and 600 mas was between 0. 31 and 0. 40 magnitude, which is equivalent to that of other state-of-the-art contrast models presented in the literature. In general, the model performed best for targets of between 5 and 9 G-band magnitude, with degraded performance for targets outside this range. This model is currently being incorporated into the Paranal SCUBA software for first-level quality control and real-time scheduling support.
2015). Given the maturity of instruments such as SPHERE, data-driven analysis and empirical models are a practical way to understand these limitations and the uncertainty that random telescope and instrumental processes have on observations. Some good examples of this are the work of  Martinez et al. (2013) , using SPHERE data to characterize speckle temporal stability in high-Strehl regimes; that of  Milli et al. (2018) , who characterized the low wind effect on SPHERE; and Jones et al. (  2022 ), who presented a datadriven analysis of SPHERE performance for faint targets. In the case of predicting contrast, various models have been explored in the literature to empirically predict the on-sky observed contrast given atmospheric and instrumental conditions  (Bailey et al. 2016; Courtney-Barrer et al.
2019; Xuan et al. 2018). In particular, initial work at the GPI used linear regression of AO telemetry and astronomical site monitoring data to predict contrast  (Bailey et al. 2016) , which was further advanced with neural networks that were able to predict the measured contrast using six input parameters available pre-observation with a contrast root mean square error (RMSE) of 0. 45 magnitude at 0. 25 ′′  (Savransky et al. 2018). Correlations between measured contrast and AO error terms have also been shown in other work (e. g. ,  Poyneer & Macintosh 2006; Poyneer et al. 2016) , and in general have been shown to provide good predictive capacity of the contrast in atmospherically limited regimes  (Fusco et al. 2016; Sauvage et al. 2016; Macintosh et al. 2014). In this work, we present a simple empirical model to predict the raw contrast measured by SPHERE with the goal to assist on-site quality control measures and short-to mid-term scheduling decisions.
On the training dataset, we also allowed for a linear calibration of the fitted intercept X f (r) with the model residuals given the partitioned instrumental state such that X(r) = X f (r) -∆ c (r|state), where ∆ c (r|state) is the training model residual when filtering for a given observational state. The state filters considered were the sky transparency classification (e. g. , thick, thin, clear, photometric), the AO gain and frequency setting, and the wavefront sensor spatial filter size (e. g. , small, medium, large). This significantly improved the cross-validation performance of the model on the training dataset, while maintaining a significant sample size for the general fitting of α parameters. These calibrated offsets were then used for the out-of-sample model test (without recalibration).
Data preparation We developed a database of all public observations taken between 2015 and 2019, which were downloaded from the ESO SPHERE archive. In this particular study, we focused on fitting the model described above for the most commonly observed SPHERE/IRDIS mode. This mode uses an apodised Lyot coronagraph with the H2 (λ c = 1. 593 µm) filter, which corresponds to the left detector in the H2/H3 dual band mode. The general FITS headers used to filter these data are displayed in Table  1. After filtering and outlier rejection (described below), train (75%) and test (25%) datasets were split to have nonoverlapping observation nights; meaning for any given sample in the train set, there did not exist a sample in the test set that was observed on the same night (and vice versa). This corresponded to 149 and 47 unique stars in the train and test sets, respectively, with only 4 stars shared between the two sets, totalling nearly 80k raw coronagraphic frames to analyze.
A 75%:25% split provided a sufficient parameter space density to perform ten-fold cross validation on the training set, while allowing sufficient samples to avoid biases in the out-of-sample test. One-sigma noise levels were estimated as a function of radius in coronagraphic data cubes (DPR TYPE = OBJECT) after some basic reduction (e. g. , background subtraction, flat fielding, bad pixel masking, and high-pass filtering). The standard deviation was calculated in an annulus with 4 pixels (∼λ/D) from 82 to 1800 mas, where radii that had pixels in the nonlinear regime of the IRDIS detector (ADU > 20k) were masked. Additionally, each coronagraphic (OBJECT) frame was cross correlated with a median coronagraphic image across the filtered dataset to provide an additional criterion for outlier removal.
The contrast curve was then calculated by dividing the 1σ noise levels at a given radius by the estimated peak flux when adjusting for the different integration times and neutral density filters. To correct for changing transparency between the noncoronagraphic (flux) and coronagraphic (object) frames, the measured contrast was multiplied by the ratio of the aggregated wavefront sensor (SPARTA telemetry) flux data between the two periods. While no specific sky classifications (e. g. , thin or thick clouds) were excluded from the data, we neglected data where there was significant (>50%) variability in the wavefront sensor flux during an exposure, because this caused significant variability in the measured contrast that was unpredictable from a pre-observation perspective. The initial contrast curve database consisted of 27 135 coadded contrast frames.
Each contrast curve was associated with various features, including the FITS headers from the initial files and the full available range of atmospheric parameters available from ESO MASS-DIMM and meteorological archives, which were interpolated to the mean coronagraphic frame timestamp. This included important parameters such as atmospheric seeing and coherence time. Atmospheric data prior to the last MASS-DIMM upgrade (April 2016) were neglected due to instrumental biases between the old and new systems. Data were then further filtered to exclude observations that were outside of standard operational conditions and/or where feature outliers were detected. Basic filters included the following: -All SPARTA AO loops were closed. -SPARTA differential tip/tilt loop is closed. -Telescope was guiding on a guidestar.
Flux model Calibrated instrumental zero points and extinction coefficients were required to estimate the photocurrent (ADU s -1 ) received in both the wavefront sensor and science detector for a given SIMBAD magnitude and airmass prior to observation. Data were first filtered to consider SPHERE flux sequences and WFS data taken during periods classified as photometric using either the LP780 or OPEN spectral filter without restrictions on the WFS spatial filter size. From these filtered data, the following model was fitted: M = β 0 2. 5 log 10 F TG + β 1 X + β 2 , ( 10 ) where M is the Simbad magnitude of the target star, F is the flux (ADU s -1 ) measured in either the WFS (G band) -which is summed over all subapertures -or the flux template from the science detector (H band), T is a scalar to represent the relative transmission of the spectral filter, G the detector gain, X   is the targets airmass, and β 0 , β 1 , and β 2 are the fitted parameters corresponding to the telescope-instrument transfer function, extinction coefficient, and zero point, respectively.
Fitted parameters from data taken in photometric conditions are outlined in Table  2  and are consistent with previously measured extinction coefficients at Paranal  (Patat et al. 2011 ). To account for sky transparency in the contrast model, sky category offsets were calibrated on the train dataset via the contrast residuals of the respective sky-category-partitioned data as described in Sect. 3. Additionally, Fig. 3  displays the results when the data were partitioned into sky-transparency categories as classified by the weather officer and the above fitted photometric model was applied to each respective sky category. Figure  4  shows that the mean absolute error between the measured and predicted WFS flux given the target magnitude scales monotonically with the weather officer classification of the sky transparency.
Model fitting The empirical contrast model presented in Sect. 3 was fitted with the Python scikit-learn package  (Pedregosa et al. 2011 ) using Bayesian regression. This model was tuned and ultimately fitted to the training dataset (75% of the data) using 10-fold cross validation. The best-fit parameters are reported as the mean of the ten-fold fit on the training set for the given radius, and respective uncertainties are ±2 standard deviations. After tuning via cross validation on the training dataset, the model was evaluated on the test dataset (25% of the data). The results are presented in the following sections. test. It can also be seen that mean model residuals were well centered near zero, with a mean error of 0. 13 mag, and the 5th and 95th percentiles in the residuals were -0. 23 and 0.
The inclusion of these data in the ETC in short exposure time limits could be used to ultimately improve the predictive accuracy of an ETC for SPHERE users. The contrast model presented here is currently being incorporated into Paranal's SCUBA software  (Thomas et al. 2020 ) to be used as first-level quality control and to help improve the real-time decision process for SPHERE at Paranal. Figure  10  shows a real example of how this model could be used in operations for providing quick checks to ensure the measured raw contrast (∼60 s frame) is within the expected 95% test residual range of the model given the target and current atmospheric conditions. Statistics on the frame-by-frame contrast could then be used to grade the OB based on potential user constraints. Abnormal aberrations caused from instrumental effects that impact the contrast can also easily be detected and evaluated by users and/or operators in the context of expected performance.
Conclusions A simple model was trained and tested on SPHERE/IRDIS coronagraphic data to predict contrast as a function of radius in the most commonly used H-band filter. When testing on out-of-sample test data, the model achieved a mean error of 0. 13 magnitude with the 5th and 95th percentiles in the residuals equal to -0. 23 and 0. 64 magnitude respectively when considering a separation of 300 mas. The test set RMSE between 250 and 600 mas was between 0. 31 and 0. 40 magnitude, which is similar to that of other state-of-the-art contrast models presented in the literature. This model is currently being incorporated into the Paranal SCUBA software for first-level quality control and realtime scheduling support. Future work will consider fitting this model to other SPHERE instrumental modes.
A Gene ontology (GO) enrichment-based cluster analysis of the list of proteins was performed using the STRING Database and MCL Clustering. We identified two enriched functional GO terms, both including CB 1 R and GAP43: GO. 0008037-Cell recognition (with matching proteins CNR1, CRTAC1, GAP43 and MFGE8), and GO. 0008038-Neuronal recognition (with matching proteins CNR1, CRTAC1 and GAP43) (p = 0. 0384 for each GO term). Based on its presynaptic localization and anatomical distribution in the central nervous system (see below), which raised the possibility of a functional interaction with CB 1 R, we focused our further analyses on GAP43. As a first approach to validate a potential CB 1 R-GAP43 interaction, we used a fluorescence polarization-based binding assay to detect direct protein-protein interactions in vitro  9  (Fig.
These data point to a restricted location of CB 1 R-GAP43 complexes within the whole hippocampus. Moreover, to evaluate a potential protein-protein interaction in synaptosomes, we performed in situ proximity ligation assay (PLA) experiments, which allow the immunofluorescence-based detection of protein complexes in fixed biological material. Hippocampal synaptosomal preparations from WT mice showed an overt CB 1 R-GAP43 PLA-positive signal, which was significantly reduced in synaptosomes from CB 1 R-deficient (Cnr1 -/-) mice (Fig. 1e ). Taken together, these observations support a physical interaction between GAP43 and CB 1 R at a selective pool of mouse hippocampal presynaptic boutons. Phosphorylation of GAP43 at S41 facilitates its interaction with CB 1 R Previous studies have shown that phosphorylation of GAP43 at S41 by protein kinase C (PKC) is critical for its biological activity  26, 29, 30.
BRET is expressed as milli-BRET units (mBU) (means ± SEM; n = 3 independent experiments). e DMR assays in HEK293T cells transfected with CB 1 R plus GAP43(WT), GAP43(S41D), GAP43(S41A) or a control empty vector, and exposed to 100 nM WIN-55,212-2 (WIN). A representative experiment is shown (n = 3 independent experiments). Source data are provided as a Source data file. 69. 9 ± 4. 4% of baseline, n = 9 cells; t (8) = 6. 91, p = 0. 0001 by twotailed paired Student's t test; GAP43(S41D): 91. 0 ± 2. 8% of baseline, n = 10 cells; t (9) = 5. 38, p = 0. 0004 by two-tailed paired Student's t test; F (2, 24) = 15. 48; Control vs GAP43(S41D) p = 0. 0002, and GAP43(S41A) vs GAP43(S41D) p = 0. 0002, by one-way ANOVA]. To directly assess CB 1 R function, we tested the effect of the CB 1 R agonist WIN-55,212-2 on extracellular MC-GC synaptic responses (i.
Proteomic spectrum files were challenged to data bases and Uniprot of sheep (Ovis aries) (UPID: UP000002356) and other mammals (Mammalian) [taxonomy_id:40674] using Proteome Discoverer software version 1. 4. 1. 14 (Thermo Scientific) and the searching tool SEQUEST (built-in version of Proteome Discoverer version 1. 4. 1. 14). In the searching criteria, carbamide methylation, cysteine nitrosylation and methionine oxidation were established as dynamic modifications. Tolerance to precursor selection and product ions was fixed at 10 ppm and 0. 5 Da, respectively. Peptide identification was validated by the Percolator algorithm (built-in version of Proteome Discoverer version 1. 4. 1. 14) using q ≤ 0. 01 (q-value being the p value additionally adjusted to the False Discovery Rate).
Representative traces were obtained by averaging 15 individual traces. Electrophysiological data were acquired at 5 kHz, filtered at 2. 4 kHz, and analyzed using IgorPro software version 7. 01 (Wavemetrics, Inc. )  25. Picrotoxin, WIN-55,212-2, AM251, D-APV and NBQX were purchased from Tocris-Cookson Inc. (Ellisville, MO, USA) and dissolved in water or DMSO. Total DMSO in the bath solution was maintained at 0. 1% in all experiments.
Walking patterns were monitored with ink-painted paws (blue, fore; red, hind) on 70-cm long paper sheets. Anxiety-like behaviors were assessed in the elevated plus-maze test. The maze consisted of a cross-shaped plastic device with two 30-cm long, 5-cm wide opposite open arms, and two 30-cm long, 5-cm wide, 16-cm high opposite closed arms, connected by a central structure (5 × 5 cm) and elevated 50 cm from the floor. First, a 5-min observation session was performed, in which each mouse was placed on the central neutral zone facing one of the open arms. The cumulative time spent in the open and closed arms was then recorded. One arm entry was considered when the animal had placed at least both forelimbs in the arm. Data are represented as the number of visits to the open arms. For the novel object recognition task, mice were habituated for 9 min in an L-maze  68.
3-100 Hz interval and amplified using a DAM50 preamplifier (World Precision Instruments, Sarasota, FL, USA). Signals were sampled at 200 Hz through an analog-to-digital converter (Power 1401 data acquisition unit, Cambridge Electronic Design, Cambridge, UK) and analyzed with Spike2 software version 10 (Cambridge Electronic Design). Each experiment began with 5 min of basal recording, followed by 15 min of recording after treatment with either vehicle or THC (10 mg/ kg; single i. p. injection). KA (30 mg/kg; single i. p. injection) was subsequently administered to induce epileptic discharges, and the hippocampal field potential was recorded continuously for the next 120 minor, occasionally, until death. An epileptic-like spike was defined as a spontaneous potential change of short duration (<100 ms) and a high amplitude (at least twice the standard deviation of the field potential baseline; typically, >20 μV).
Statistics Data are presented as mean ± SEM, and the number of experiments is indicated in every case. Statistical analysis was performed with Graph-Pad Prism version 8. 0. 1 (GraphPad Software, San Diego, CA, USA). All variables were first tested for normality (Kolmogorov-Smirnov's and Shapiro-Wilk's test) and homocedasticity (Levene's test) before analysis. When variables satisfied these conditions, one-way or two-way ANOVA with Tukey's or Sidak's post hoc test, or two-tailed paired or unpaired Student's t test, was used as appropriate and indicated in every case. We considered p values <0. 05 as statistically significant. Power analysis was performed with IBM SPSS software version 28 (IBM, Bois-Colombes, France).
Acknowledgements This work was supported by the  Spanish Ministerio de Ciencia e Innovación (MICINN/FEDER ; grants  RTI2018-095311-B-I00  and  PID2021-125118OB-I00  to M. G. , SAF-2017-87629-R to E. I. C. and V. C. ,  PID2020-113938RB-I00  to E. M. and V. C. , BFU 2017-83292-R to J. S. -P. , and  PID2020-119358GB-I00  to D. F. d. S. ) and the  NIH  (grants  R01 MH116673 ,  R01 MH125772 ,  R01 NS115543 , and  R01 NS113600  to P. E. C. ). L. B. and G. M. were supported by  INSERM. I. B. M. and C. C. -I. were supported by contracts from the  Spanish Ministerio de Universidades (Formación de Profesorado Universitario Program , references  FPU15/01833  and  FPU16/ 02593 , respectively). We are indebted to  Rodrigo Barderas-Machado ,.
Data availability All data generated or analyzed during the study are included in this article, its Supplementary Information, and its Source data file. Proteomic spectrum files were challenged to data bases and Uniprot of sheep (Ovis aries; UPID: UP000002356) and other mammals [Mammalian; taxonomy_id: 40674] using Proteome Discoverer software version 1. 4. 1. 14 (Thermo Scientific) and the searching tool SEQUEST (built-in version of Proteome Discoverer version 1. 4. 1. 14). Source data are provided with this paper.