dataesr/scientific-paragraphs-categorization huggingface dataset:
98 mm, L ± 0. 3 mm, DV -2. 3 mm; and (NAc core) AP + 1. 34 mm, L ± 1 mm, DV - 4. 6 mm. Behavioral testing. To ensure unbiased data collection, mice from the various experimental groups were randomly assigned to cages, and the experimenters remained blind to their group allocation throughout the study. Meal pattern measurements were performed automatically, eliminating the possibility of experimenter biases in data recording and analysis. The rst behavioral test performed was the limited access which spanned two consecutive weeks, followed by the HFD quinine adulteration test. Limited Access to HFD. This test was carried out speci cally on overweight mice from the SC + HFD group to evaluate in exibility and compulsiveness. During the rst week, the mice were administered with saline solution 30 min prior to being given free access to the HFD for three consecutive days.
Staining with cFos was performed with rabbit anti-cFos (1:1000, Sc-7202, Santacruz) and visualized with anti-rabbit Alexa-647 (1:500; A-21443, Thermo Fisher Scienti c). mCherry and EGFP were directly visualized without uorescence ampli cation. Cell counting. A 20x/0. 70 NA objective was used to acquire confocal uorescence images using a Leica TCS SP5 inverted scanning laser microscope. Using ImageJ software and a z-stack (3 µm step size), cFos + cells were counted both in the PL and NAc region. Within the PL, a region of interest (ROI) was manually delineated according to the mCherry and DAPI signal in each section. In the NAc, a ROI was manually delineated according to the Paxinos and Franklin atlas  [23]. The number of cFos + cells was then divided by the volume of the delineated region.
A negative water control was included in each run. The thermal cycling was initiated at 95°C for 10 min followed by 45 cycles of 10 s at 95°C and 15 s at 55ºC, the optimal annealing temperature for our target genes. Melting curve analyses were carried out at the end of each run of qPCR to assess the production of single, speci c products. Statistical analysis. Most variables of interest were analyzed using linear mixed models to account for the dependence of the data within the same animal. The time course of body weight gain was analyzed using linear mixed models with the following xed effects: diet (SC consumption of the SC group, SC and CM separate consumption of the SC + CM group, SC and HFD separate consumption of the SC + HFD group), genotype, and their interaction. A random intercept per mouse was considered.
Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of electricity load. In this study, we propose a method based on stationary wavelet transform (SWT), quantile regression (QR), Bidirectional nested long short-term memory (BiNLSTM), and Depthwise separable convolution (DSC) combined with attention mechanism for electricity consumption probability prediction methods. First, the data sequence is decomposed using SWT to reduce the complexity of the sequence; then, the combined neural network model with attention is used to obtain the prediction values under different quantile conditions.
The final CBAM output is shown as follows: F = F F t (14) where represents the multiplication of corresponding elements. Finally, multiply the spatial attention and F element-wise to get the final output feature F. In this paper, DSC is combined with CBAM, namely ADSC, where SAM allows the DSC network to focus more on the mutation regions in the data sequence that play a decisive role in the prediction effect and ignore unimportant information, while CAM considers the relationship between feature map channels. After two attention mechanisms, the prediction effect of the model will be improved. The convolutional block attention module (CBAM) is one of the more popular attention mechanisms in image processing  [37]. Traditional convolutional neural networks use the same convolution for all feature channels, ignoring the different levels of importance of the information contained in the different channels, resulting in lower training accuracy.
TCN can effectively grasp the change law of stationary signal in timing under the case of a few model parameters. This method is applied to predict low-frequency components. In contrast, the high-frequency signal obtained from the decomposition still contains both frequent and complex fluctuations, and it is difficult for a single model to capture the complex non-linear features. Therefore, a combined model with high robustness, ADSC-NLSTM, is proposed and applied to the high-frequency section. The ADSC-NLSTM model is composed of two parallel networks, BiNLSTM and ADSC-CBAM, which capture the characteristics of data from time and space, respectively. Among them, CBAM weights the feature map obtained by the DSC model, highlights the feature information in the feature map that has a greater effect on the prediction performance and improves the prediction ability of high-frequency signals.
The multidimensional feature data obtained from the TCN-ADSC-NLSTM model after extracting the high and low-frequency components are fed into the fully connected layer, and then the quantile regression is used, i. e. , the loss function of the prediction model is set to a quantile loss function according to Equation  (16). change law of stationary signal in timing under the case of a few model parameters. This method is applied to predict low-frequency components. In contrast, the high-frequency signal obtained from the decomposition still contains both frequent and complex fluctuations, and it is difficult for a single model to capture the complex non-linear features. Therefore, a combined model with high robustness, ADSC-NLSTM, is proposed and applied to the high-frequency section. The ADSC-NLSTM model is composed of two parallel networks, BiNLSTM and ADSC-CBAM, which capture the characteristics of data from time and space, respectively.
Dataset Description The open-source data we use comes from a project called "UK-DALE"  [41]. This dataset records power consumption data for each appliance and five households' total electricity consumption. The entire dataset spans from 22:28:15 on 9 November 2012 to 18:35:53 on 26 April 2017, where the sampling interval is 6 s. We take the cumulative sum of 50 consecutive sampling points each time to obtain the 5-min interval data. We selected the first 9600 data samples for each household as the amount of data varies between households. The electricity consumption data of each household was divided into a training set and a test set according to the ratio of 87. 5% and 12. 5%.
Interval Prediction Results Analysis To verify the effectiveness of the interval prediction results, the results of the interval prediction evaluation indexes of the nine models at a confidence level of 95% are shown in Table  2. And it can be seen from the table that the interval coverage of the model proposed in this study is the highest compared with other models and has high reliability. The PIAW metric of our proposed model is the lowest among all tests, with reductions of 48. 72%, 51. 02%, 44. 72%, and 53. 34% compared to the QRLSTM, QRGRU, QRNLSTM, and QRCNN-LSTM models, respectively. This is due to the pre-processing of the data by SWT. SWT-QRADSC-NLSTM has a 13. 38% reduction in PIAW compared to SWT-QRNLSTM, which indicates that the improvement in interval prediction performance depends not only on the quadratic decomposition of the SWT but is also determined by the structure of.
The CRPS metric values for the probabilistic prediction results of the different models are shown in Table  2. The CRPS of the model proposed in this paper is the smallest in all five household datasets, indicating that this model has the highest comprehensive probabilistic prediction performance. In graphical form, we present the results and evaluation metrics for point prediction, interval prediction, and probabilistic prediction: (1) There is a significant improvement in point prediction accuracy compared to the cutting-edge DL method. The MAPE values have been reduced to below 4% for all five different patterns of household energy consumption forecasts. (2) Most of the actual values fall within the high confidence level prediction interval, reflecting the validity of the model interval prediction proposed in this paper.
Conclusions Residential energy consumption forecasting is essential for the electricity market and individual households. Researchers have been striving to implement new methods to improve prediction accuracy. SWT is a more reliable decomposition method, TCN can extract multi-scale features, BiNLSTM can obtain time-series dependencies, DSC can better learn the useful features, and CBAM can focus on the critical information of the model. These methods have good performance in power forecasting. QR is a reliable method commonly used in interval forecasting. Therefore, a new hybrid model SWT-QRTCN-ADSC-NLSTM is constructed in this paper to forecast household energy consumption. The DL model's multi-channel input data is constructed using the SWT method, which reduces the complexity of the data.
2  corresponds to 160 to 200 averages for a total acquisition time of 4 to 6 h. For the experiments with HYL1 bound to a miRNA precursor, the protein samples were mixed with in vitro transcribed miR172a precursor in a 1:1 ratio. The DEER time traces were analyzed using Deer Analysis  [29, 30]. The data shown in Fig. 2C  correspond to the DEER time traces after background correction. The distance distributions were obtained using a Tikhonov regularization factor of 250. The error was estimated using the DeerAnalysis validation tool.
HYL1 ensembles generation and PRE calculations 2. 7. 1. Generation of conformers for the ensemble The initial 100 models for the LBT containing constructs were generated using Modeller v 9. 19  [33]. We constructed 100 initial models using as templates the crystal structures for each domain of HYL1 (3ADG and 3ADJ for D1 and D2, respectively) and the crystal structure of a Ca (II) loaded LBT (PDB 1TJB). Then we selected the best model based on the DOPE parameter. Starting from this model we generated ensembles by producing new conformers using PyRosetta  [34]. Briefly, a residue among those defined as movable is picked randomly. Then the Phi and Psi angles are randomly selected from a library of random coil values provided with the software Flexible Meccano  [35]. The energy of the new structure is evaluated by measuring the fa_atr score.
Conformer clustering In order to select a subensemble of conformers that fits the experimental data, we clustered the conformers based on the similitude of calculated I/I 0 profiles. The I/I 0 profiles for the domains directly bound to the LBT are fairly constant (see Section 3. 3 in Results) and hence are not influenced by the conformational sampling of the domains with respect to each other. Therefore, the distance between two models was defined as the squared sum of the differences between the I/I 0 values of each residue for D1 (positions 18 -88) considering the C-terminal Gd (III) and for the D2 (positions 104 -174) considering the N-terminal Gd (III). The clustering processes with each distance matrix were done with the python library scipy. cluster. hierarchy. linkage in the "ward" mode.
Bioinformatic analysis of linker sequences To obtain sequences of dsRBD containing proteins, we searched the database UniProt  [38]  with the query "dsRBD". We obtained 50,188 proteins and recorded their sequence, domains position and taxonomy data. We kept sequences corresponding to metazoans and viridiplantae harboring two or more dsRBDs for further analysis (Supplementary Table  1 ). We then measured the lengths of linker sequences between consecutive dsRBDs (Supplementary Figure  3 ). The animal proteins did not show any particular length conservation. In contrast, the plant proteins presented a high conservation of linkers length of 17 residues (Fig. 1A ). The high number of sequences with 17-residue linkers could be related to biases in plant species representativity in the database.
Modelling the conformational propensities of HYL1 The experimental results obtained show that in spite of being structurally independent within the full protein, the two domains of HYL1 do have restrictions in terms of their relative orientations and distances. In order to understand the structural basis of these restrictions we modelled an ensemble of physically plausible protein conformations to then find the ones that best explain the observed interactions. For modeling, we considered the domains as rigid bodies and allowed for conformational flexibility mainly on the linker. We constructed 100 initial models of L-D1-D2-L with the software Modeller using the crystal structures for each domain of HYL1 (3ADG and 3ADJ for D1 and D2, respectively  [7] ) and the crystal structure of a Ca(II) loaded LBT (PDB 1TJB  [43] ) as templates.
Acknowledgements This work was supported by grants  PICT 2012-1702 ,  PICT 2013-3281  and  PICT 2017-2807  from  ANPCyT, Argentina , and the mobility grants  PICT-CNRS DEERNA  and  ECOS-Sud A14B02. FCM received a doctoral Fellowship from  CONICET, Argentina. We benefited from the biophysics platform of the  Institute for Integrative Biology  of the Cell supported by  French Infrastructure for Integrated Structural Biology  ( FRISBI ,  ANR-10-INBS-0005 ), the  Infrastructure for Biology, Health and Agronomy (IBISA)  and the  Ile-de-France DIM-ELICIT program.
INTRODUCTION Over 70,000 meteorites have been registered by the Meteoritical Society to date, and this number has increased by more than 2000 per year since 2014 (see the Meteoritical Bulletin Database, https://www. lpi. usra. edu/ meteor/). The classification of so many meteorites is a significant burden for the meteorite scientist community, all the more because 90% of the non-Antarctic meteorites (1250 meteorites for the year 2020 for instance) are classified by only 10 scientists worldwide  (Gattacceca et al. , 2021). The majority of these meteorites are ordinary chondrites (84% in 2020). The classic tools for meteorite classification are petrography (optical microscopy and/ or scanning electron microscopy) and electron probe microanalyses (EPMA). Petrographic observations are an unavoidable step for the classification of any meteorite.
g. ,  Keil & Frederiksson, 1964) , or by their metal content, be they equilibrated or not (e. g. ,  Jarosewich, 1990). Chondrule size can also be used to distinguish between unequilibrated H, L, or LL chondrites (e. g. ,  Metzler, 2018). Oil immersion, that has been widely used for the classification of equilibrated ordinary chondrites (EOC) collected in the framework of the ANSMET program  (Lunning et al. , 2012) , is now being abandoned as it resulted in relatively frequent misclassification due to the uncertainty on the estimate of silicate composition. X-ray diffraction allows the classification of EOC, but remains relatively time-consuming and has significant uncertainty  (Di Cecco et al. , 2022). Handheld XRF has been shown to distinguish successfully the three ordinary chondrite groups based on their bulk Fe/Mn ratio and is another powerful tool to distinguish some achondrite groups  (Zurfluh et al.
5 A m À1 in amplitude. There are two calibration factors for this instrument. The first is a factor of sensitivity α (in m 3 kg À1 ), and the second is a dimensionless factor related to the size of the sample β that is a function of the mass m. Using these two factors, the instrument estimates the massnormalized magnetic susceptibility as χ m m ð Þ ¼ αK β m ð Þ : (4) The sensitivity factor α was determined by measuring flat concrete blocks that were large enough to be "Meteorite meter" considered as semi-infinite (the raw data are available in Table  S2 ). The susceptibility of these blocks was also measured using a KLY-2 magnetic susceptibility meter from Agico operating at 920 Hz. This provided a value α = 3. 58 × 10 À3 m 3 kg À1 (Figure  2a ). Smaller samples provide smaller apparent magnetic susceptibilities, because a larger fraction of the sensitive zone of the coil is occupied by air (Figure  2b ).
It is noteworthy that although the MetMet operates at a different field amplitude and frequency compared to the KLY-2 and SM30 instruments that have been widely used to assemble meteorite susceptibility reference databases, no significant frequency dependence has been detected in most meteorites where it was tested (e. g. ,  Gattacceca et al. , 2014  for ordinary chondrites, and our own unpublished data). Similarly, no significant field dependence has been detected (our unpublished data) nor is it expected. Therefore, the susceptibility measurements performed with the MetMet can be safely compared to the published susceptibility values that have been measured with other instruments (e. g. ,  Rochette et al. , 2003 Rochette et al. , , 2008 Rochette et al. , , 2009)).
Samples and Methods We measured a total of 162 meteorite samples belonging to 25 meteorite groups (the data available in Table  S6 ). The average sample mass is 39 g, ensuring that the measurements are representative of the bulk meteorite. This allowed exploring the logχ m versus Cindex space covered by different groups. In a second step, we targeted specifically weathered ordinary chondrites (n = 143, average mass 97 g; Table  S7 ), to study the effect terrestrial weathering on both parameters. All measured samples are from the CEREGE meteorite collection. Finally, 54 terrestrial rocks and minerals were measured for comparison with meteorites (Table  S8 ). The statistics are calculated by a Python script using Numpy, Scipy, Matplotlib, and Pandas modules (the script and the raw data are available in Material S2).
Acknowledgments-We thank the two reviewers,  S. Tikoo  and  T. Kohout , and the Associate Editor,  K. Joy , for their detailed and constructive reviews of this manuscript. This study was partially supported by the  Agence Nationale de la Recherche  (project  FRIPON , number  ANR13-BS05-0009 ) and the  Vigie-Ciel outreach program. Mariko  U. wound up all of the MetMet pickup coils, measured the calibration basalt pebbles, and tested each unit after assembly. Editorial Handling- Dr. Katherine Helen Joy.
10. 1029/2022JE007494 3 of 31 the MEDA's Skycam camera) provides a global picture of the surroundings of the rover. Combining data from different MEDA sensors, we are able to discriminate DDs from other DL events (such as DL by wind gusts) or establish when a particular vortex is lifting dust. In this work, we analyzed observations acquired by MEDA, in particular by the Radiation and Dust sensor (RDS), during the first 365 sols to constrain the DD frequency of formation at Jezero crater, as well as to derive DD properties such as DD diameter, trajectory, or advection velocity. In order to better constrain the DD properties, the observations were analyzed with RT simulations and combined with measurements made by other MEDA sensors (e. g. , pressure). We describe the data used in this work, the RT model, the principle of detection, and simultaneous detections with other instruments in Section 2.
Following the detections sequence described above, Figure  5  shows the rover and RDS sensor orientations during this event, the sun position (263° with respect to North clockwise), and the possible trajectory of the DD. The information about the rover position and orientation as well as the sun location was obtained from the ancillary data of M2020 (also available at NASA's Planetary Data System). The rover Yaw angle measured from North was 127. 84°, hence the RDS Lat-1 channel was pointing at 118. 84° (as this channel points 9° to the left of the rover's longitudinal axis). The trajectory was derived by assuming a straight line crossing the different Lat sensor detections, along with the direct light-blocking occurring when all Top sensors have the greatest drop in their corresponding signals.
Discussion and Conclusions The MEDA instrument onboard Mars 2020 Perseverance rover has collected a unique set of in situ meteorological data from Jezero crater (C. E. Newman et al. , 2022)  and with this data set, in particular using MEDA-RDS observations, DL events such as DDs were studied for the first 365 sols of the mission (half a martian year). A principal advantage of using the MEDA-RDS data set for this study is its sampling frequency (1 Hz) and temporal coverage (see Figure  3 ), which allowed us to study the DD properties encompassing the full diurnal period. The period between 12:00 and 13:00 was when the maximum of detections occurred, with no other maxima found. Among all RDS detections, ∼70% of the cases were classified as DL events (including any DL event occurring on the surface) and ∼61% as DDs.
Data Availability Statement All perseverance data used in this study are publicly available via the Planetary Data System (J. M. Maki, 2021; Rodriguez-Manfredi & de la Torre Juarez, 2021). The radiative transfer simulations, dust devil retrievals, and derived data of  12, 14, 15,[17][18][19] 21, 22, and C2  are available in an archive located at  Toledo (2022) , version 2.
We present the combination of the Canada-France-Hawaii Telescope (CHFT) Large Area U-bands Deep Survey (CLAUDS) and the Hyper-Suprime-Cam (HSC) Subaru Strategic Program (HSC-SSP) data over their four deep fields. We provide photometric catalogs for u, u * (CFHT-MegaCam), g, r, i, z, and y (Subaru-HSC) bands over ∼20 deg 2 , complemented in two fields by data from the Visible and Infrared Survey Telescope for Astronomy (VISTA) Deep Extragalactic Observations (VIDEO) survey and the UltraVISTA survey, thus extending the wavelength coverage toward near-infrared with VIRCAM Y, J, H, and K s observations over 5. 5 deg 2. The extraction of the photometry was performed with two different softwares: the HSC pipeline hscPipe and the standard and robust SExtractor software. Photometric redshifts were computed with template-fitting methods using the new Phosphoros code for the hscPipe photometry and the well-known Le Phare code for the SExtractor photometry.
2021) , selection of lowluminosity galaxies  (Cheng et al. 2021) , photometric redshift estimation  (Sawicki et al. 2019; Moutard et al. 2020; Huang et al. 2020) , and constraining galaxy-galaxy merger fraction evolution  (Thibert et al. 2021). These earlier CLAUDS+HSC-SSP catalogs already demonstrated the usefulness of a deep, U-band-enhanced wide-area photometric dataset in the HSC-SSP Deep and ultraDeep fields. However, because of their developmental nature, we did not make these earlier catalogs publicly available to the community. Moreover, the depth of grizy imaging used in their creation was significantly shallower than what has become available as the HSC-SSP survey accumulated data over time. Finally, for simplicity, our earlier catalogs did not include any NIR information even though deep NIR imaging.
The images were resampled using Swarp  (Bertin et al. 2002) , the background subtracted using a 128 × 128 pixel mesh grid done with Swarp, and stacked to produce the images and weight maps matching the HSC patches. In the case of the NIR data, the fully calibrated public mosaics (image and weight maps) are projected onto the HSC pixel grid with Swarp for all the patches covered by the NIR data. For the VIDEO data, some tiles are duplicated due to the layout and overlap of the different observations. The duplicated tiles are combined by averaging them over the overlaps to obtain a single tile per patch. The U and NIR band processed data are formatted into coadd files that can be handled by hscPipe (see  Bosch et al. 2018). A82, page 4 of 25 This is performed with a module developed for hscPipe 4.