dataesr/scientific-paragraphs-categorization huggingface dataset:
The optimisation of the model was carried out through a trial-and-error procedure in which the model parameters were modified and a goodness-of-fit measure between the model results and the calibration dataset was checked  [53]. This process was repeated iteratively to obtain the best possible fit between observed and predicted data, until the following criteria were met: R 2 ¼ max MAE ¼ min lim n!1 ða ¼ 0&b ¼ 1Þ ð7Þ 8 > < > : The goodness-of-fit 0�R 2 �1 (optimum) was maximised to quantify the strength of a linear relationship between observations and estimates, while the second condition minimises the mean absolute error (optimum) 0�MAE<1. Due to the limited calibration sample and the lack of a direct validation dataset, the third condition applied is to minimise the regression line distortion between observations and estimates from the 1:1 identity line (unit slope and null intercept) as the number of samples (the vector of data) increases.
Spreadsheetbased modelling was developed using the free online statistical software STATGRAPHIC (http://www. statpoint. net/default. aspx), with graphical support from AnClim (http://www. climahom. eu/software-solution/anclim  [69] ) and CurveExpert Professional 1. 6 (https:// www. curveexpert. net). In order to also estimate the gross soil erosion (SGE) for comparison with the tolerable soil loss, the SGE in the basin was predicted as: SGE ¼ EUSEM ORB SDR ð8Þ with SDR the sediment delivery ratio, calculated according to Diodato and Grauso  [70] : SDR ¼ 0:416 � S 0:5 � ffi ffi ffiffi A p � ð500 À ffi ffi ffi E p Þ � P a � �À 0:422 ð9Þ where S is the mean slope (0. 16), A is the area (2657 km 2 ), and E is the mean elevation (346 m a. s. l. ) of the basin, and P a is the mean annual recipitation (924 mm) over the basin  [71].
Scale bar = 100 µm, inset scale bar = 20 µm. n Bar plot showing the percentage of GFP-positive neurons ± SEM (n cells = 338, n rats = 2) in the vlPAG stained and not stained by GAD67 antibody. Data are represented as mean ± SEM and as individual paired points. Source data are provided as a Source data file. from -40 to 0 s). This WU effect is typically enhanced during pain perception in animals with inflammation  20  , which suggests that WU can serve as an index of ongoing nociceptive processing (i. e. , a measure of how sensitive the body is to nociceptive stimuli at a given moment). Therefore, we used WU (represented as the percentage of maximal spiking activity following electrical stimulation (1 Hz) to the hind paw receptive field) as our outcome measure for the effect of vlPAG-BL stimulation on WDR discharge, specifically from primary afferent C-fibers (Supplementary Fig.
Scale bar = 20 cm. m Graphs showing the ΔCPP score (left) and total distance traveled (right) for the test and control groups. Unpaired t-test (two-sided): p = 0. 6185 (left) and p = 0. 3587 (right); n = 6 per group. All results are expressed as average ± SEM and individual animals are represented with the lines, or individual points represented as blue or white circle. Source data are provided as a Source data file. PVN OT → vlPAG OTR circuit promotes analgesia in the chronic neuropathic pain model condition and in both male and female rats, whereas previous work found that the parvOT → SC WDR circuit does not  3. Moreover, considering that the vlPAG is an important area for the regulation of various defensive behaviors  32  , and that OT can mediate defensive behaviors, it will be important for future research to determine if vlPAG OTR neurons might be involved in other functions beyond nociception.
Hela cells were transfected with a combination of one of the px330 plasmids and the OXTR-specific reporter vector. After transfection, the Cas9-nuclease-induced double-strand breaks stimulated the repair of the lacZ gene segments into a functional reporter gene, the activity of which was determined in cell lysates using an o-nitrophenyl-β-D-galactopyranosid (ONPG) assay. A luciferase expression vector was also added to the transfection mix and luciferase activity was measured for normalization. The most effective gRNA target site including PAM was determined as 5'-ACTCCAGTCTTCCCCC GTGGTGG-3'. Specificity of the CRISPR/Cas induced genomic modification. The CRISPOR program was used to identify potential off-target sites for the selected OXTR gRNA (5'-ACTCCAGTCTTCCCCCGTGGTGG-3'). No offtarget sites were detected in an exonic sequence or on the same chromosome.
0 (ACD). OTR and Cre mRNA-expressing cells in the PAG region from one brain hemisphere per section, were counted with an ImageJ Cell Counter plugin. Neurons were identified by the presence of a DAPIstained nucleus and/or an unambiguous cell-like distribution of fluorescent mRNA dots. Three-dimensional reconstruction and analysis of OT-OTR contacts in the PAG Confocal images were obtained using a Zeiss LSM 780 confocal microscope (1024 × 1024 pixel, 16-bit depth, pixel size 0. 63-micron, zoom 0. 7). For the three-dimensional reconstruction, 40-µm-thick z-stacks were acquired using 1 µm-steps. Imaris-assisted reconstruction was performed as previously described  21, 47, 55. In brief, surface reconstructions were created based on the four individual channels (DAPI, OT, OTR-GFP, SYN/vGlut2). Co-localization of OT signal with SYN or vGlut2 was confirmed both manually and through the association/overlap function of IHC-labeled puncta in the Imaris software.
0), mounted with a X-Light Confocal Unit-CRESTOPT spinning disk. Images were acquired at 2 Hz with an optiMOS sCMOS camera (Qimaging). The fluorescent focal planes were illuminated for 30 ms (OGB1: 475 nm) in bright-field mode using a Spectra 7 LUMEN-COR. The different hardware elements were synchronized through the MetaFluor 7. 8. 8. 0 software (Molecular Devices). The fibers were stimulated using a 575 nm wavelength illuminated for 30 ms at 20 Hz for 30 s after 5 min of a baseline period. Using the Fiji software, the brightness and contrast parameters were automatically adjusted. As no cell could be clearly identified, the variations of fluorescence were measured on the whole plane recorded. Further offline data analysis was performed using a custom-written Python-based script available on the editorial website.
Patch clamp recording. We targeted GFP+ or GFP-neurons in the vlPAG for whole-cell patch-clamp recording. The recording pipettes were visually guided by infrared oblique light video microscopy (DM-LFS; Leica). We used 4-9 MΩ borosilicate pipettes filled with a Kgluconate-based solution composed of (in mM): KMeSO 4 (135), NaCl (8), HEPES (10), ATPNa 2 (2), GTPNa (0. 3). The pH was adjusted to 7. 3-7. 4 with KOH and osmolality was adjusted with sucrose to 290-310 mOsm/L, as needed. Data were acquired with an Axopatch 200B (Axon Instruments) amplifier and digitized with a Digidata 1440 A (Molecular Devices, CA, USA). Data were sampled at 20 kHz and lowpass filtered at 5 kHz using the pClamp10 software (Axon Instruments). Further analysis was performed using Clampfit 10. 7 (Molecular Devices; CA, USA) and Mini analysis 6 software (Synaptosoft, NJ, USA) in a semi-automated fashion (automatic detection of events with chosen parameters followed by a visual validation).
Data were acquired on an MC Rack recording unit (Multi Channel Systems), and spikes were sorted by Wave Clus  56. Spike data was analyzed with custom MATLAB (MathWorks) scripts and the MLIB toolbox (Stüttgen Maik, Matlab Central File Exchange). The firing rate of each recording unit was smoothed by convolution of Gaussian distribution, whose width was 10 s and standard deviation was 5. The mean firing rate of the baseline period (BSmean) was defined as 0% activity and subtracted from the firing rates (FR) of the whole period (FR-BSmean). Maximum absolute activity (max(FR-BSmean)) was found using the highest absolute value among moving means of (FR-BSmean) with a time window of 21 s. When maximum absolute activity was found to exceed the BSmean, it was defined as 100% activity (cell#1~21), whereas when maximum absolute activity was found to be below BSmean, it was defined as -100% activity (cell#22~23).
We further normalized these data so that the plateau phase of the maximal WU effect was represented as 100 percent activity. As WU is dependent on C-fiber activation, it can be used as a tool to assess nociceptive information in the SC and, in our case, the anti-nociceptive properties of OT acting in the vlPAG. We recorded WDR neuronal activity using the following protocol: 40 s of hind paw electric stimulation to induce maximal WU followed by continued electrical stimulation to maintain WU while simultaneously delivering 20 s of vlPAG blue light stimulation (30 Hz, 10 ms pulse width, output ~3 mW), followed by another 230 s of electrical stimulation alone to observe the indirect effects of OT on WU in WDR neurons. Electrical stimulation was ceased after the 290 s recording session to allow the WU effect to dissipate.
Drugs All drugs used in this study are listed in the reagents and resource table. pain conditions (Fig. 6 ). Behavioral data are expressed as mean ± standard deviation (SD). Statistical tests were performed with Graph-Pad Prism 7. 05 (GraphPad Software, San Diego, California, USA) and MATLAB. For CFA and CCI (Fig. 6  and Supplementary Fig. 10 ) animals, a two-way ANOVA with repeated measure followed by multiple comparisons post hoc tests with Dunnett correction was used. The multiple comparisons were done within each condition (baseline, antagonist, recovery) by comparing each time point (blue light, 1 h, 3 h) to its own control. The same statistical procedure was used for CFA animals after DCZ administration (Fig. 6  and Supplementary Fig. 10 ). The ΔCPP score was calculated with the following formula in order to control for time spent in the neutral chamber: ΔCPP score = (paired postcondunpaired postcond )-(paired hab -unpaired hab ).
Acknowledgements This work was supported by the  Centre National de la Recherche Scientifique  contract  UPR3212 , the  Université de Strasbourg  contract  UPR3212 ; the  University of Strasbourg Institute for Advanced Study (USIAS)  fellowship  2014-15 ,  Fyssen Foundation  research grant  2015 ,  NARSAD   Young Investigator Grant   24821 ,  Agence Nationale de la Recherche  ( ANR ,  French Research Foundation ) grants  n°19-CE16-0011-0 ,  19-CE37-0019 , and  20-CE18-0031  (to A. C. ); the  Graduate School of Pain EURIDOL ,  ANR-17-EURE-0022  (to A. C. and E. C. C. );  ANR-DFG  grant  GR 3619/701 ,  PHC PROCOPE  and  PICS07882  grants (to A. C. and V. G. );  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) grants  GR 3619/15-1 ,  GR 3619/16-1  (to V. G. ); SFB Consortium  1158-2  (to V.
G. , S. H. , and B. D. );  French Japanese governments fellowship B-16012 JM/NH and Subsidy  from  Nukada Institute for Medical and Biological Research  (to M. I. );  Fyssen Foundation  fellowship (to A. L. );  Région Grand Est fellowship  (to D. K. );  DFG Postdoc Fellowship   AL 2466/1-1  (to F. A. ); the  Foundation of Prader-Willi Research post-doctoral fellowship  (to C. P. S. and F. A. ); DAAD Postdoc Short term research grant  57552337  (to R. P. );  DFG Walter Benjamin Position -Projektnummer 459051339  (to Q. K. ). National Heart, Lung, and Blood Institute  Grant  NIH HL090948 ,  National Institute of Neurological Disorders and Stroke  Grant  NIH NS094640 , and funding provided by the  Center for Neuroinflammation and Cardiometabolic Diseases (CNCD) at Georgia State University  (to J.
j Bar graph showing that the vast majority of OT fibers within the vlPAG are vGluT2negative (92. 4%). n = 4. k, l 3D reconstruction of contacts between an OTR dendrite and OT fibers. k Co-localization of OT (magenta) and vGluT2 (red) are indicated by white arrowheads. l Mismatch of OT (magenta) and vGluT2 (red) are indicated by white arrowheads with an asterisk. DAPI = blue, OTR = green. n = 4 female rats. Scale bars in order of appearance: 50, 10, 10, 20, and 20 µm. Results are expressed as the mean ± SEM and the individual points of each conditions are represented as white circle. Source data are provided as a Source data file.
Section 5 describes the effects on temperatures produced by a regional dust storm that covered Jezero between sols 312 and 318 (L S ∼ 153°-156°). A brief comparison of the MEDA thermal data with temperatures observed by other missions and predicted by models is carried out in Section 6. The conclusions are summarized in Section 7. Two Appendices discuss technical aspects of the thermal contamination sources (Appendix A) and a selection algorithm to obtain environmental temperatures from raw Air Temperature Sensors (ATS) data (Appendix B). Martian Local True Solar Time (LTST) is used to discuss times throughout this paper.
Data Description and Sensor Characteristics The MEDA instrument  (Rodriguez-Manfredi et al. , 2021)  measures environmental variables around the clock in sessions that can be as short as 5 min or as long as several hours to adapt to mission requirements. Typically, MEDA covers over 55% of each sol following a measurement pattern, planned using Local Mean Solar Time, that covers even hours in even-numbered sols and odd hours in odd-numbered sols (see Figure  S1  in Supporting Information S1). Thus, in most cases, the combination of data from two consecutive sols allows us to fill in complete diurnal cycles. The full data set allows us to study timescales ranging from those associated with atmospheric turbulence to diurnal cycles and their seasonal evolution. The MEDA data sets that are involved in the analysis of temperatures are provided by the ATS, the Thermal Infrared Sensor (TIRS), and the Wind Sensors (WS).
, 2017; Rodriguez-Manfredi et al. , 2021). This requirement was tested in calibration experiments and was confirmed after landing on Mars with real data, for example, rapid temperature increases are observed coinciding with close passages of vortex cores  (Hueso et al. , 2023; Newman et al. , 2022). The ATS data accuracy is better than ±1 K, with a resolution including noise better than 0. 1 K  (Rodriguez-Manfredi et al. , 2021). Compared to other missions, the lower thermal inertia of MEDA's ATS enables faster response times and more precise data. For instance, VL1 and VL2 had narrow thermocouple sensors at a single altitude  (Hess et al. , 1977) , MPF had a mast with thermocouple sensors at three elevations above the surface  (Schofield et al. , 1997) , and PHX had thermocouples at three altitudes above the lander deck  (Davy et al.
Comparisons of simultaneous ATS1-3 and wind data suggest that even smaller winds are able to couple the sensors to the atmosphere (Appendix A). ATS4 and ATS5 are also decoupled from the rover's thermal boundary layer when winds come from the frontal and lateral side of each sensor. However, ATS4, which is located behind Perseverance's warm 7-foot-long Robotic Arm (not shown in Figure  1a ), is less exposed to environmental winds and typically records a higher temperature than ATS5. A comparative analysis of ATS measurements with wind data and the rover's orientation (Appendix A) reveals that, in most cases, the minimum temperature of the three ATS at the RSM can be considered a reliable measure of the environmental temperature. The minimum of ATS4 and ATS5 might still have some thermal contamination because winds from a wide range of directions cannot completely decouple these sensors from the thermal boundary layer of the rover.
In order to preserve the thermal fluctuations in the data, we use an algorithm that analyzes the signals from the five ATS and produces temperatures at 0. 84 and 1. 45 m, following the lower temperature at each altitude and sliding gradually from one sensor to another while preserving temperature fluctuations (Appendix B). Finally, thermal perturbations from the RTG can affect the ATS measurements on the RSM when winds of ∼2-4 ms -1 blow from the back of the rover  (Gómez-Elvira et al. , 2012; Lorenz & Sotzen, 2014). The two sensors at 0. 84 m on the front of the rover are shielded from RTG effects and serve to identify episodes of RTG contamination. Several observations of RTG contamination events combined with simultaneous wind data show that these are typically short duration events that occur mostly during the night and do not have an overall impact in the measurements of the daily cycle of temperatures (Appendix A and Figure  S3  in Supporting Information S1).
08 and 0. 45 K  (Rodriguez-Manfredi et al. , 2021; Sebastián et al. , 2021). The upward looking channel is strongly perturbed by the solar radiation when the sun enters its Field of View (FoV). This circumstance is labeled with the corresponding flag in the TIRS data files allowing such data to be filtered-out from the analysis. The TIRS ground temperature channel has a FoV that covers an ellipse of about 3 m 2 pointed ∼3. 75 m away from the RTG to minimize thermal contamination  (Pérez-Izquierdo et al. , 2018). The ground temperature is measured integrating the upward long-wave infrared flux from 8 to 14 μm  (Sebastián et al. , 2020). Because surface emissivities can vary from site to site and are unknown, they are not accounted for in the instrument calibration and, as a result, retrieved ground brightness temperatures can be slightly lower than actual kinetic temperatures  (Sebastián et al.
, 2020 (Sebastián et al. , , 2021)). Based on past experience with similar sensors on MSL, the largest uncertainties in ground temperatures coming from surface properties are estimated to be ∼3 K in the early morning and ∼5 K near noon  (Hamilton et al. , 2014). Winds are recorded by the WS with two booms that are azimuthally separated by 120° to enable accurate wind retrievals regardless of wind direction. This is necessary because both booms are attached to the rover's RSM, which perturbs winds approaching from the rear of each boom  (Rodriguez-Manfredi et al. , 2021). Each boom contains six thermal anemometers in which a cold die and four hot dice are exposed to the environment, and the electrical current required to keep the hot dice temperature constant is measured. The wind data retrieval is based on calibration activities in a wind tunnel.
A boom selection algorithm chooses which of the two booms provides the closest to the true environmental winds at any moment; these selections are available in the NASA Planetary Data System (PDS) data set within the derived data products (DER files). Specific analysis of TIRS and WS data beyond their relationship with temperatures are presented by  Martínez et al. (2023) ,  Vicente-Retortillo et al. (2022) ,  Newman et al. (2022)  and Viúdez-Moreiras,  Lemmon, et al. (2022) , Viúdez-Moreiras, de la  Torre-Juarez, et al. (2022).
Diurnal Cycle of Temperatures Complete diurnal temperature cycles at four altitudes (surface, 0. 84 m, 1. 45 m, and 40 m) can be obtained by combining ATS and TIRS data from consecutive sols that share similar conditions, for example, the same rover orientation and similar surface properties. Representative examples at different L S are given in Figure  2. Derived ATS temperatures at 0. 84 and 1. 45 m between sols 34 and 40 (L S ∼ 25°) are shown in Figure  2a. This figure shows that the diurnal temperature cycles change very little in consecutive sols. Figures  2b-2d  show diurnal temperature cycles based on averages over time windows of 5 min at the surface, 0. 84, 1. 45, and 40 m and at L S values of 25°, 90°, and 180°, extending beyond results presented in  Rodríguez-Manfredi et al. (2023).
The greater variability found in the comparison of these plots is observed in surface temperatures, and is driven by a combination of seasonal changes and differences in local thermophysical properties of the explored terrains. Table  1  highlights in a numeric form the characteristic temperatures of each period in Figures  2b-2d. Vertical thermal profiles can be obtained at different times. Figure  3  shows an example of the diurnal evolution of the vertical thermal profile for sols 186-194 (L S = 92-96°). This figure ignores the data at 0. 84 m due to the lower reliability of ATS4-5 at night. During daytime hours, the solar irradiation heats the surface and part of this heat is transferred as sensible heat to the atmosphere, leading to convective instability and the growth of a Convective Boundary Layer (CBL)  (Martínez et al.
The highest temperature fluctuations appear to occur at 1. 45 m but note that, as described above, the ATS at 0. 84 m are largely sheltered from the environmental winds and may not capture the full intensity of thermal fluctuations. Temperature fluctuations at ∼40 m captured by TIRS are smaller at noon than those observed at 1. 45 m, in agreement with  Mason and Smith (2021) , with this reduction of turbulence with height likely caused by mixing as air parcels ascend. At night, temperature fluctuations are also present in the data. Typical values of nighttime fluctuations are below 0. 5 K. Both TIRS at 40 m and ATS at 1. 45 m result in similar values of the nighttime temperature fluctuations, which are possibly related to changing winds and nighttime shear-driven turbulence. Seasonal patterns of temperature fluctuations are not evident in the MEDA data so far.
Temporal Evolution of Daily Temperatures We first examine diurnal ATS and TIRS temperature data. To calculate averages over 24 hr, we combine data from two consecutive sols, which together usually cover the entire LTST range (see  Section 2). Then, we compute Fourier fits for the diurnal cycle data using 8 harmonics. This is done at each of the four altitudes. The Fourier fits allow us to calculate mean daily temperatures as well as maximum and minimum temperatures removing the effects of oscillations and minor gaps in the data. Fits for each pair of sols are examined visually to filter out pairs of sols where calibration activities of TIRS or large data gaps may produce anomalous behaviors. In addition, the Fourier fits allow the thermal tide components of the diurnal thermal cycle to be examined.
e. , average difference below 1 K). MEDA data at 0. 84 m are systematically warmer than MCD predictions by 2 K as a result of residual thermal contamination affecting ATS4-5 (see Section 2), especially at night. The progressive increase of temperatures observed at all levels as the summer advances (Figure  7a ) follows closely the cycle of solar irradiance at the top of the atmosphere, with the daily average of the irradiance at the top of the atmosphere peaking at L S ∼ 144° (sol 295) at Jezero, close to the peak in mean surface temperatures. A comparison of temperatures with irradiances is shown in Figure  S5  in Supporting Information S1 using Equation  1 from  Vicente-Retortillo et al. (2015)  to calculate solar fluxes at Jezero. The daily amplitude of the diurnal temperature cycle at each altitude is shown in Figure  7b  for MEDA data and MCD predictions.
Temporal Evolution of Temperatures as a Function of L S and LTST We now examine how temperatures evolve both as a combined function of L S and LTST. Figure  8  shows temperatures at Jezero averaged in windows of 30 min as a function of L S and LTST. In this figure, an interpolation over MEDA data in both L S and LTST has been used to cover gaps in the data. We show ATS temperatures at 1. 45 m (panel a) and surface temperatures from TIRS data (panel b), which are more reliable and have a lower number of gaps than temperature data at 0. 84 and 40 m, respectively. The difference between surface temperatures and air temperatures at 1. 45 m (panel c) constitutes a visualization of the evolution of the vertical thermal gradient at different hours through the 400 sols. Overall, both daytime temperatures at the surface and at the 1.