dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other hand, quantitative evaluation of high-resolution Mars topographic products (≤10 m/pixel) is always a difficult task as there are currently no high-resolution, high-accuracy and large spatial coverage topographic data that can be considered as "ground-truth" available for Mars. A recent Mars DTM evaluation study  [64]  examined the relative effective resolution and accuracy of target DTMs (at lower nominal resolutions, i. e. , 20-50 m/pixel from CTX and HRSC) with respect to a set of reference DTMs (at higher nominal resolution, i. e. , ~1 m/pixel from HiRISE) that are considered as having much higher effective resolution and vertical accuracy (20 times or more) than the target DTMs. This assumption is made based on the conjecture that even though the differences between the target and reference DTMs arise from errors of both datasets, the errors of the reference dataset can be considered as negligible due to the reference DTMs having much higher resolution (as much smaller features can be resolved) and thus much higher accuracy than the target DTM.
The HiRISE PDS DTM and HiRISE MADNet DTM have the highest accuracy and structural similarity with respect to the reference HiRISE SRR MADNet DTM, which is partially due to fact that they are produced from the same imaging dataset. Comparing the statistics of the CaSSIS SRR MADNet DTM and CaSSIS MADNet DTM, we can observe a fairly significant improvement of effective resolution using SRR, while having a slightly lowered accuracy and structural similarity, meaning slightly larger topographic uncertainty of the CaSSIS SRR MADNet DTM.
For example, features with confusing albedo/shadow may confuse the SDE model and produce artefacts, especially for uncommon surface features that are not included in the training dataset which can occasionally be falsely interpreted as inverted topography. Difficult to view areas (e. g. , features within cast shadows) may result in lower topographic accuracy and possibly introduce new artefacts. There appear to be effects associated with differences in solar incidence angles which may result in height variations. We plan to optimise and generalise the proposed method in the future to improve the reliability for Mars global applications. Last but not least, the proposed deep learning-based method is computationally less costly for large-sized orbital images such as HiRISE, compared to using traditional regularisation based SRR  [33]  and photoclinometry  [22, 25]  techniques.
shows the footprints of the HiRISE (blue, foreground) and CaSSIS (green, background) images (in Mollweide projection-see https:// pro. arcgis. com/en/pro-app/latest/help/mapping/properties/mollweide. htm (accessed on 22 December 2021)), up to 2 September 2021 and 28 August 2021, respectively. colour bands). CaSSIS images are typically sampled at 4 m/pixel nominal spatial resolution (~4. 6 m/pixel effective spatial resolution) with a swath width of about 9. 5 km and a swath length of about 30-40 km. Due to the non-sun-synchronous orbit and its 74° inclination angle, CaSSIS is able to image sites at different local times of different seasons, making CaSSIS images an ideal dataset for studying the Martian surface changes. At the time of the writing of this paper, CaSSIS images covered 4. 3% of the Martian surface (by 28 August 2021).
Acknowledgments: The research leading to these results is receiving funding from the  UKSA Aurora programme  ( 2018-2021 ) under grant  ST/S001891/1 , as well as partial funding from the  STFC MSSL  Consolidated Grant  ST/K000977/1. S. C. is grateful to the  French Space Agency CNES  for supporting her HiRISE related work. Support from  SGF (Budapest) , the  University of Arizona (Lunar and Planetary Lab. ) and  NASA  are also gratefully acknowledged. Operations support from the  UK Space Agency  under grant  ST/R003025/1  is also acknowledged by S. C. , S. X. has received funding from the  Shenzhen Scientific Research and Development Funding Pro-gramme  (grant No. JCYJ20190808120005713 ) and  China Postdoctoral Science Foundation  (grant No. 2019M663073 ). S. X. has received funding from the  Shenzhen Scientific Research and Development Funding Programme  (grant No.
Funding: The research leading to these results is receiving funding from the UKSA Aurora programme (2018-2021) under grant no. ST/S001891/1 as well as partial funding from the STFC MSSL Consolidated Grant ST/K000977/1. S. C. is grateful to the French Space Agency CNES for supporting her HiRISE related work. PRo3D-based analysis and visualization is funded by the Austrian Space Applications Programme Project 882828 "PanCam-3D 2021".
Experiments were conducted under 2D closed-loop conditions, i. e. , rotations of the ball displaced the visual stimuli only laterally. To this end, we used a custom software developed using the Unity engine  (version 2018. 3. 11f1) , opensource code available at https://github. com/G-Lafon/BeeVR (Lafon et al. , 2021). The software updated the position of the bee within the VR every 0. 017 s.
5 µl of 10 µmol of each primer and 2 µl of ultrapure water. The reaction conditions were as follows: 95 • C for 30 s followed by 40 cycles of 95 • C for 10 s, 55 • C for 30 s and a final step at 95 • C for 10 s followed by a melt curve from 55 • C to 95 • C with 0. 5 • C per second. The reaction was performed in a CFX384 Touch Real-Time PCR Detection System (Bio-Rad) and analyzed with the software Bio-Rad CFX Manager. Each sample was run in triplicates. If the triplicates showed too much variability (SD > 0. 3), the furthest triplicate was discarded. If the two remaining triplicates still showed too much variability (SD > 0. 3) the sample was discarded. The samples were subjected to a relative quantification and normalization. First for each sample and for each reference gene per brain region, the relative quantity (Qr) was computed using the difference between the mean Ct value of each sample and the highest mean Ct value ( Ct), using the following formula: Qr = (1 + E) Ct (with E = efficiency of the reaction).
Behavioral Data The first choice of the bees was recorded during the conditioning trials and the non-reinforced test. In this way, we established for each trial and test the percentages of bees choosing first each of the stimuli displayed or not choosing a stimulus (± 95% confidence interval). Test percentages were analyzed within groups by means of a generalized linear mixed model (GLMM) for binomial family in which the individual identity (Bee) was considered as a random factor (individual effect) while the choice category (CS + , CS-, and NC) was fitted as a fixed effect; z values with corresponding degrees of freedom are reported throughout for this kind of analysis. For the acquisition trials, we recorded motor variables such as the total distance walked during a trial, and the walking speed.
Gene Expression Data Statistical differences in gene expression were assessed for reference genes to check for stability and for target genes within a given brain region using One-Factor ANOVA for independent groups in the case of multiple comparisons or two-sample t test in the case of dual comparisons. Post hoc comparisons between groups were performed by means of a Tukey test following ANOVA. No cross-comparisons between brain regions or genes were performed due to within-area normalization procedures. Statistical analyses were done either with R 3. 5. 1 software (R Development Core Team, 2016) or with Statistica 13 Software (TIBCO Data Science).
ACKNOWLEDGMENTS We thank the valuable feedback of  Takeo Kubo  ( University of Tokyo ) and  Hiroyuki Okuno  ( Kagoshima University, Japan ) on our IEG analyses. We also thank  Shiori Iino ,  Hiroki Kohno ,  Benjamin H. Paffhausen ,  Marco Paoli , and  Dorian Champelovier  for valuable discussions and support, and two reviewers for useful comments on a previous version.
2, inverse results will be shown in Sect. 3 with an emphasis on the global budget and on the seasonal cycle of the optimized fluxes. In Sect. 4, the fluxes will be prescribed to the LMDz atmospheric transport model, and the resulting concentrations will be evaluated against independent observations over North America, the Pacific Ocean, Japan and France. We will also compare the simulated concentrations against Michelson Interferometer for Passive Atmospheric Sounding (MIPAS)  (Fischer et al. , 2008)  retrievals over the tropics. Finally, we will discuss the potential and limitations of this inverse system to constrain the GPP with COS observations. 2 Data and method.
, 2007). After corresponding forward runs that defined the tracer linearization trajectories, the adjoint model was run 9 months backward in time from measurement time for each of these synthetic observations (with appropriate y * ), giving as output the series of integrated sensitivities of the corresponding measurement with respect to the surface fluxes throughout the 9 months and to the concentrations at the initial point in time. For times prior to 9 months, we have in fact not used the exact adjoint values. Instead, we extended the databases of adjoint outputs for the surface fluxes beyond the 9-month windows with two parts: (i) monthly adjoint outputs between months 9 and 24 taken from computations for the year 2017 and (ii) beyond 24 months, a globally homogeneous value (i. e. , 1 GtC emitted at the surface is translated to an average concentration of 0.
Independent observations An ensemble of independent observations -i. e. , data that are not assimilated in LMDz -is used to evaluate the fluxes retrieved by our inverse system. We focus here on the observations used to evaluate the COS and the GPP fluxes. The first observation programme is the HIAPER Pole-to-Pole Observations (HIPPO;  Wofsy, 2011). HIPPO consisted of five aircraft transects of many trace gas measurements, including for COS and CO 2 , in the troposphere over the western Pacific: HIPPO 1 (January 2009), HIPPO 2 (November 2009), HIPPO 3 (March-April 2010), HIPPO 4 (June 2011) and HIPPO 5 (August 2011). The HIPPO measurements were made from flask and in situ measurements by NOAA and the University of Miami. They were rescaled to be consistent with the calibration scale used for the NOAA surface network results.
The IMK/IAA retrieval processor operated at KIT-IMK was used to calculate the COS profiles of data version V5R_OCS_221/222, which were used for this work  (Glatthor et al. , 2015 (Glatthor et al. , , 2017)). The number of vertical layers of the MIPAS retrievals is 60. Between altitudes of 7 and 25 km the accuracy of the COS profiles is around 50 ppt in the absence of clouds (in particular deep-convective ones)  (Glatthor et al. , 2015). Last, the SIF satellite retrievals from the Global Ozone Monitoring Experiment-2 (GOME-2) make it possible to evaluate the seasonality of GPP inferred by inverse modelling for each PFT. SIF represents the amount of light reemitted by chlorophyll molecules as a byproduct of photosynthesis. Satellite-based SIF data are considered a proxy for the GPP of terrestrial ecosystems at large spatial-temporal scales  (Frankenberg et al.
, 2011; Guanter et al. , 2012; Zhang et al. , 2016; Yang et al. , 2015; Li et al. , 2018). We use release number 28 of the NASA GOME-2 (Global Ozone Monitoring Experiment-2 on board the MetOp-A satellite) daily corrected SIF product  (Joiner et al. , 2013 (Joiner et al. , , 2016)). The dataset is available at https://avdc. gsfc. nasa. gov/pub/data/satellite/ MetOp/GOME_F/v28/ (last access: 7 February 2022). We used the monthly level 3 product gridded at a 0. 5 • resolution between the years 2008 and 2019. This GOME-2 SIF product was shown to be very similar in terms of seasonality and magnitude (after spectral scaling) to the reference Orbiting Carbon Observatory (OCO-2, launched in 2014;  Sun et al. , 2018)  data  (Bacour et al. , 2019). For each PFT, we average all the grid points within the LMDz grid points that have a fractional cover greater than 0.
Data sampling For each species and each measurement, the simulated concentration fields were sampled at the LMDz 3D grid box closest to the observation location. As mentioned above, the observations at selected local times are assimilated as 8 d av-erages. For the independent observations, LMDz is sampled at the closest time from the observations. All observations are dry-air mole fractions calibrated relative to the compound World Meteorological Organization (WMO) mole fraction scale. Satellite retrievals are dry-mole fractions tuned by the data providers to the compound World Meteorological Organization (WMO) mole fraction scale. For comparison, the corresponding dry-air variables in the model simulations are used. When comparing MIPAS data with LMDz simulations, the a priori and vertical sensitivity of the retrievals must be taken into account.
CO 2 fluxes Our reference fluxes combine several information sources. Fossil fuel emissions are from the gridded fossil emission dataset GCP-GridFED (version 2019. 1) (Jones et al. , 2021). Biomass burning fluxes vary inter-annually and are described by the GFED 4. 1s database (https://www. globalfiredata. org/ data. html, last access: 7 February 2022). Monthly air-sea CO 2 exchange is prescribed from the Copernicus Marine Environment Service database  (Denvil-Sommer et al. , 2019). The GPP and respiration fluxes have been simulated at a resolution of 0. 5 • in both longitude and latitude by the OR-CHIDEE land surface model  (Krinner et al. , 2005). OR-CHIDEE explicitly parameterizes the main processes influencing the water, carbon and energy balances at the interface between land surfaces and atmosphere.
Biomass burning We use the inventory emissions from  Stinecipher et al. (2019)  with a global estimate of 60 ± 37 GgS yr -1 for the period 1997-2016. These authors used CO as a reference species to compute the COS biomass burning emissions. To do that, they combined emission factors of COS to CO from the literature and applied them to the CO emissions. These CO emissions were computed beforehand from the GFED Global Fire Emissions Database (GFED version 4, https://www. globalfiredata. org/, last access: 7 February 2022). The resulting biomass emissions are classified into four categories: savanna and grassland, boreal forests, temperate forests, tropical deforestation and degradation, peatland fires, and agricultural waste burning. The savanna was shown to be the largest contributor to the global biomass burning emissions and therefore to the overall uncertainty.
Comparison with independent atmospheric observations As a second step, we assess the a posteriori concentrations using several datasets: the MIPAS satellite retrievals, the HIPPO airborne measurements, and the surface measurements over Japan and France (see Sect. 2. 2). In particular, the MIPAS retrievals of COS atmospheric concentrations at 250 hPa in the tropics give insight into the magnitude of the main biospheric sink located over Brazil during the wet season, when convective air masses reach the upper troposphere  (Glatthor et al. , 2017). First, Fig. 9  shows the a posteriori and a priori COS seasonal concentrations at 250 hPa, convolved with the MIPAS averaging kernels and averaged over the period 2009-2012. We see that the inversion reduced the RMSE by more than one-third throughout the whole year.
Miller, Fred Moore). The airborne measurements from HIPPO have been made with the help of Eliott L. Atlas at the University of Miami (for the HIPPO campaign). Dan Yakir facilitates the collection of flask samples at WIS. Shohei Hattori facilitates the collection of flask samples over Japan. Financial support. This research has been supported by the H2020 Innovation In SMEs (grant no. 776186). Review statement. This paper was edited by Anita Ganesan and reviewed by J. Elliott Campbell and one anonymous referee.
For the LRU values, we have only made a distinction between C 4 and C 3 plants. A complementary experiment would be to optimize a set of LRU coefficients for each PFT together with the GPP fluxes. We plan to include the PFT dependence of the LRU by using the LRU dataset ofMaignan et al. (2020)  derived from a mechanistic vegetation model and for which conductances will be further tuned with eddy covariance flux measurements. LRU absolute values are indeed critical. For instance, if the LRU were larger at high latitude, the inversion would not need to increase the GPP as much. However, LRUs have been estimated to be lower in the boreal ecosystems (around 1 and 1. 8 forMaignan et al. (2020)  andSeibt et al. (2010), respectively) than in the tropical and temperate ecosystems (around 1. 3 and 3 forMaignan et al.
Final ABS spectrum Average over all snapshopts each mode q r,α k , performing a TD-DFT computation and a fragment-based diabatization (FrD) with a maximum-overlap criterion implemented in our "in house" code Overdia. For this reason we indicate this LVC Hamiltonian with the label FrD-LVC. This step (evidenced in red in Figure  2 ) is the most time consuming because it requires 2 × N r vib + 1 TD-DFT computations for the dimer, (where N r vib is the number of stiff normal modes), plus the computation of the necessary overlaps between the transition densities necessary to apply the diabatization scheme.
DFT/TD-DFT usually offers a good compromise between accuracy and computational cost. However, in order to adopt our approach as a predictive tool, i. e. for species not yet synthesized, it might be beneficial to run a preliminary becnhmark analysis on similar systems to select the best DFT functional. Alternatively, it is in principle possible to couple our approach with different electronic-structure methods. This potentiality has already been shown for pyrene, adopting our code Overdia to parameterize an LVC Hamiltonian with multiconfigurational methods with perturbative corrections, like RASPT2. 95 Extensions in combination with methods of the coupled cluster family, like the promising domain-based local pair natural orbital similarity transformed equation of motion-coupled cluster singles and doubles (DLPNO-STEOM-CCSD) are also possible.
Acknowledgements The authors thank  Javier Cerezo ,  Universidad Autónoma de Madrid , for the many helpful discussions on the implementation of Ad-MD|gLVC method, and  Marcel Nooijen ,  University of Waterloo  for suggesting to compare the obtained results with those predicted by a simple average  Hamiltonian. DA acknowledges Fundación Ramón Areces (Spain)  and  Generalitat Valenciana/European Social Fund  ( APOSTD/2021/025 ) for fundings. M. P and A. S. thank the financial support from the  COMETE  project  (COnception in silico des Matériaux pour l'EnvironnemenT et l'Energie ) co-funded by the  European Union  under the program ' FEDER-FSE Lorraine et Massif des Vosges   2014-2020.
Functional classification of proteins from sequences alone has become a critical bottleneck in understanding the myriad of protein sequences that accumulate in our databases. The great diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of the evolution of living organisms and for biotechnological applications. ProfileView is a sequence-based computational method, designed to functionally classify sets of homologous sequences. It relies on two main ideas: the use of multiple profile models whose construction explores evolutionary information in available databases, and a novel definition of a representation space in which to analyze sequences with multiple profile models combined together.
ProfileView classifies protein families by enriching known functional groups with new sequences and discovering new groups and subgroups. We validate ProfileView on seven classes of widespread proteins involved in the interaction with nucleic acids, amino acids and small molecules, and in a large variety of functions and enzymatic reactions. ProfileView agrees with the large set of functional data collected for these proteins from the literature regarding the organization into functional subgroups and residues that characterize the functions. In addition, ProfileView resolves undefined functional classifications and extracts the molecular determinants underlying protein functional diversity, showing its potential to select sequences towards accurate experimental design and discovery of novel biological functions.
Introduction Functional classification of biological sequences is fundamental to understanding the ever-increasing genomic and metagenomic sequence data accumulating in our databases. This quest depends on the correct domain annotation of coding genes  (Ponting and Dickens 2001; De Filippo et al. 2012; Prakash and Taylor 2012)  which, in the past, was handled by sequence homology and featurebased approaches. The first and most intuitive approach searches for sequences homologous to already known protein or domain sequences  (Hawkins et al. 2006; Wass and Sternberg 2008; Loewenstein et al. 2009; Clark and Radivojac 2011; Törönen et al. 2018 ) and does so either by direct pairwise sequence alignment or by passing through protein signatures, which are descriptions of protein or domain families derived from multiple sequence alignments.
2012; Boari de Lima et al. 2016; Cao and Cheng 2016; Zhang et al. 2017; Kulmanov and Hoehndorf 2020) , with the evident dependence on the availability of different data-types and a large and highly diversified dataset of sequences. Novel computational approaches that classify sequences by function and overcome the intrinsic limitations of existing methods would help screen sequences to design accurate experiments directed to functional testing and to discover new functions. ProfileView is a computational method conceived for this purpose, capable of classifying hundreds/thousands of homologous sequences into functional groups. It is strongly based on the understanding of the structure of sequence data imposed by the evolutionary history of the sequences. The first main step of ProfileView is to encode functional and structural information belonging to the protein family into multiple profile models that capture the diversity of the homologous sequences in the family.
Based on the set of different models for the family, the second main step of ProfileView is to define an original sequence space which organizes sequences by function. Biologically interpretable information and functional motifs are extracted from the classification process. In other words, family members are organized in a tree structure, where subfamily delineation is possible thanks to the hierarchical organization. The presence of multiple functions in a family or subfamily makes it desirable to subdivide its members into smaller groups in order to capture differences in function-related features at a lower level than the subfamily. ProfileView representative models and their specific conserved motifs have proven to be good indicators of this functional delineation. ProfileView can be applied on a large scale to a wide variety of datasets.