dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, this DNA fragment containing the regulatory/promoter sequence of the Mex gene, as formerly reported  47  has been inserted through the EcoR1 and DraII sites into the pattB-Sf-HS-GALGS (Sfi1 orientated) (kindly provided by H. Tricoire, Paris). The transgenic flies have been generated by BestGenes (USA), in which the plasmid-construct has been inserted in VK02 site located at position 28E7 on the second chromosome. The Mex-GS transgenic line has been re-introduce in the deletion background by standard genetic cross and recombination. Genesis of the UAS-snoRNA lines (jou, sno-2, sno-3). First, the snoRNA jou of 148 bp was cloned into the TOPO-TA cloning vector (Invitrogen) using the same primers used for RT-PCR (see Suppl. Information for snoRNA-Fb and snoRNA-Rb). This construct was also used to synthesize probes for ISH.
Then, both reactions were migrated in parallel on an 8% acrylamide gel + 8 M urea, at 2000 volts during 2 h. After drying, the gels were then subjected to phosphoimager analysis using a Typhoon Trio Imaging System (GE Healthcare) for 2 h. Determination of the cells number of different cell types. First, to determine the number of cells of the different cell types, the gut were dissected in PBS and then fixed in 4% PFA for one hour, followed by 3 washes with PBS and then stained with DAPI for 5 min, and finally mounted in Mowiol. To determine the number of ECs, we count the total number of nucleus stained with DAPI on microscope images taken on two distinct areas of a 33,000 µm 2 in the posterior midgut. The displayed data represent the means of these two distinct areas. Second, the number of each other specific cell types were counted on the whole gut.
postech. ac. kr/oasis2/)  70. For the qPCR, and the quantification of number of different intestinal cells, data were analysed statistically using analysis of variance (one-way ANOVA) followed by a TUKEY test, with Statistica TM software. We declare to have complied with all the relevant ethical regulations for animal testing and research. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  E. Heard  for her help with the search of the mammalian orthologue sequences. F. Rouyer  ( NeuroPSI, Gif-sur-Yvette ),  A. Molla-Herman ,  J. R. Huynh , and more especially  A. Bardin  ( Institut Curie, Paris ) for their helpful comments and critical reading of the paper. B. A. Edgar  ( Heidelberg, Germany ),  X. Zeng  ( NCI/Frederick, USA ), and  G. H. Thomas  ( Penn. State Univ. , USA ) for the gut-Gal4 lines,  H. Tricoire  for the pattB-Sf- HS-GALGS (Gene-Switch vector ), and  M. N. Soler  from  Imagif CNRS  for her assistance with confocal microscopy. We also thank  M. Betermier  and her  Team  ( CNRS, I2BC, Gif-sur-Yvette ) for the loan of sequencing/pseudouridylation materials. This work has benefited from the facilities and expertise of the high throughput sequencing core facility of  I2BC (Centre de Recherche de Gif  -http://www.
For the ocean carbon, the spin-up starts from a physical climate model simulation (without carbon) that is almost at equilibrium after it has been integrated for approximately 3,000 years and in which the surface of the ocean is at steady state whereas there is a small warming drift at depth of about 0. 006°C over 150 years. The ocean carbon spin-up initial conditions are taken from Global Ocean Data Analysis Project Version 2 (GLODAPv2) for DIC and alkalinity and from World Ocean Atlas 2013 (WOA2013) for nitrate, silicate, and iron while the atmospheric CO 2 concentration is at preindustrial levels. The model experiences an initial adjustment of about 50 years while the surface carbon distributions adjust to the physical ocean mixing and exchanges with the atmosphere and while the biological pump comes to near steady state.
Uncertainties in CarbonTracker terrestrial biosphere CO 2 flux are large in some regions, particularly in high latitudes in the NH, and may reach the same magnitude as the signal itself. This is partially due to scarcity of ground observations, in particular, over Siberia and in the Amazon region. For this reason, although the comparison of model to CarbonTracker flux data provides a decent assessment of the model's skill, such a comparison of magnitudes in regional scale is not entirely conclusive, and the relative patterns should be considered taking into account the regional uncertainties of both datasets.
Acknowledgments We thank  Larissa Nazarenko  for ocean spin-ups and the  GISS IT team (Neal Sofge ,  Angel Suarez ,  Sabrina Hosein , and  Wasim Shaikh ) for support. Funding was provided by the  NASA   Modeling, Analysis and Prediction (MAP) program :  NNX16AC93G  under PI Romanou;  12-MAP 12-0062  under PI Kiang; and ModelE support grant  NNH10ZDA001N  under PI Gavin A.
Data Availability Statement Data used in this study are available on Earth System Grid Federation (https://esgf. llnl. gov). Citations for data used in this study are as follows: NASA-GISS GISS-E2-1-G-CC model output prepared for CMIP6 (CMIP: https://doi. org/10. 22033/esgf/cmip6. 11657; C4MIP: https://doi. org/10. 22033/esgf/cmip6. 11656; CMIP-historical: https://doi. org/10. 22033/esgf/cmip6. 11762; C4MIP esm-1pctCO2: https://doi. org/ 10. 22033/esgf/cmip6. 11719; and CMIP piControl: https://doi. org/10. 22033/esgf/cmip6. 11856); NASA-GISS GISS-E2. 1G model output prepared for CMIP6 (C4MIP: https://doi. org/10. 22033/esgf/cmip6. 2060; C4MIP 1pctCO2-rad: https://doi. org/10. 22033/esgf/cmip6. 6958; C4MIP 1pctCO2-bgc: https://doi. org/10. 22033/ esgf/cmip6. 6956; C4MIP hist-bgc: https://doi.
Detailed analysis of the sequential order of these rotamerizations suggests that an allosteric mechanism, involving the outward motion of an asparagine residue in transmembrane helix 3, might be a prerequisite to the large scale conformational transition of CXCR4. This case study provides the proof-of-concept that the correlation methods developed here are valuable exploratory techniques to help decipher complex reactional pathways. With more than 150,000 protein structures deposited in the Protein Data Bank (PDB) by the end of 2019, structural biology has provided breakthrough advances in the functional mechanisms of numerous proteins and in drug design 1. The crystallographic structures deposited in the PDB give examples of the large amplitude conformational changes that proteins can undergo under specific conditions.
The acceleration factors were calculated from E dihed_avg , the average dihedral energy, and N, the number of atoms in the system, according to: where the acceleration parameter λ was set to 0. 3  27, 41. During both the cMD and aMD simulations, the length of each step was 2 fs. Quantitative analyses were performed with the R package Bio3D  42. Graphical analysis of the trajectory was carried out with the VMD program  43. Figures were prepared with the PyMOL Molecular Graphics System, version 1. 8 (Schrödinger, LLC). To facilitate comparison with other GPCRs, the position of each residue is followed by a superscript indicating its Ballesteros' numbering  32. correlation scores. The flow chart developed to compute correlation scores between sidechain dihedral angles is displayed in Fig. 1. First, using the R package Bio3D 42 , the Cartesian coordinates of the heavy atoms of the protein were transformed into dihedral angles to build the dihedral matrix which gives the chi dihedral angles of each sidechain (except Gly and Ala) for each frame of the trajectory (488 dihedral angles for 1200 frames in the trajectory under scrutiny).
Analysis of correlation scores. The OMES and CIRCULAR scores and the subsequent functions developed for data analysis were written in the R programing language and are available in the R package Bios2cor (version 2. 1) which can be found in the Comprehensive R Archive Network (cran. r-project. org). The correlation/covariation scores between the dihedral angles were stored in squared matrices (here 488 × 448 matrices) whose all diagonal values were set to 0. Scores of correlation/covariation between dihedral angles within the same sidechain (autocorrelation) were set to 0 (lowest score for both methods) because we were interested in studying dihedral correlations between different sidechains. Thereafter, for clarity purpose, the term "correlation" will be used for both scores. For comparative purposes, the raw scores were normalized by computing Z-scores (i.
e. , a score is now represented by its number of standard deviations to the mean  51  ). This normalization effectively removes the scale of measurement without making any hypothesis about the shapes of the distributions. 4) CIRCULAR(X i , X j ) = circular(X i , X j ) 2. (5) OMES(X i , X j ) = 1 K x,y (N obs x,y (X i , X j ) -N exp x,y (X i , X j )) 2 We performed two types of analysis. First, we focused on the top ranking pairs and we analyzed their networking structure. The visualization of the network between top ranking pairs of dihedral angles was carried out with the Cytoscape software (version 3. 7)  52. This analysis was limited to the top 25 pairs-a number that represents about 5% of the angles. In these graphs, angles are represented as nodes and correlation scores as edges, the node sizes are proportional to the number of links, and the edge sizes are proportional to the correlation scores.
S1 ). As a matter of fact, the first principal component represents, respectively, for OMES and CIRCULAR, about 25% and 15% of the data covariance. So, CIRCULAR might be the first choice for exploring a whole trajectory, whereas OMES might lead to a wider coverage of correlated rotamerizations in link with a single transition in (part of) a trajectory. Consensus results should shed light on important sidechain motions in complex multistep transitions. (5) More subtle differences are observed, mainly for the second or the third principal components. CIRCULAR displays more variability in highlighted angles. For example, the 202. chi1 angle interconverted between the (trans, g +) and (trans, g-) rotamers before and after the transition, respectively. By contrast, the 190. chi1 angle remained in the same rotameric state, but its average values changed from -80 ± 11° to -62 ± 10° upon the transition (Fig.
Acknowledgements This study was supported by the  ChemoTX_ProG Grant  from the  French National Research Agency  to  MC  ( ANR-11-BSV2-026 ), and by institutional grants from  INSERM ,  CNRS  and  University of Angers. This work was granted access to HPC resources of IDRIS (GENCI Grant  100567  and E-biothon access to MC). BT was supported by a fellowship from  ANR. MC is supported by  CNRS.
Data availability All the data used for this article have been deposited at the Mendeley Data Repository (https ://doi. org/10. 17632 /5v7bm fctsz. 1). The description of the deposited data is given in Supplementary File S1. The scripts developed for this study have been integrated into the R package Bios2cor (version 2. 1) that is available at the Comprehensive R Archive Network (https ://cran. r-proje ct. org/web/packa ges/Bios2 cor/index. html).
MATERIALS AND METHODS Data collection Southern elephant seals, Mirounga leonina (Linnaeus 1758), were tagged in two locations. In October 2017 and 2018, three and two post-breeding female SES on the Kerguelen Islands (49°18′S, 70°32′E; KER) were equipped with a head-mounted DTAG-4 sound and movement tag (dimensions 97×55×33 mm, 200 g in air), a neck-mounted Argos tag (SPOT-293 Wildlife Computers, 72×54×24 mm, 119 g in air) and a back-mounted CTD-Fluorescence tag (SMRU-SRDL, 115×100×40 mm, 680 g in air). In October 2018, a further two individuals were tagged at Península Valdés, Argentina (PV) following the same protocol. Animals were anaesthetised while hauled out and tags were glued to the pelage using quick-setting Araldite adhesive with a low exothermal reaction. Total handling time of each animal was less than 1 h, and vital signs were monitored carefully throughout the procedure.
Data analysis The tags were configured to start operating soon after animals left the haul-out and so began recording while the seals were still in shallow continental shelf waters for an interval of 3-24 days. Although seals may forage opportunistically as they head towards deeper water, the large majority of foraging in the 2-3 month postbreeding trip occurs in deeper waters; therefore, the analysis focused on dives taking place beyond the shelf break, i. e. in waters deeper than 1000 m  (Guinet et al. , 2014). All data analyses were conducted in Matlab (version R2018b) using custom-written code and functions from www. animaltags. org.
Prey capture attempts Prey capture attempts (PCAs) were detected in the acceleration data using the root mean square (RMS) of the norm jerk, i. e. the RMS of the vector magnitude of the rate of change in the 3-axis acceleration  (Ydesen et al. , 2014). The RMS jerk was computed from the full bandwidth (200 Hz sampling rate) acceleration data using an averaging time of 40 ms and an overlap between averages of 20 ms to give an output sampling rate of 50 Hz. A delay-corrected symmetric finite impulse response (FIR) filter was used for the averaging. RMS jerk histograms for each animal showed a bimodal distribution with a minimum value of about 350 m s -3 separating the low and high jerk modes (Fig. S1 ). The low jerk mode was associated with swimming and resting behaviours while the higher jerks occurred as brief transients during deep dives and so presumably indicate PCAs.
Light data processing Sections of overloaded light data or light not recorded at the maximum preamplifier gain (i. e. corresponding to the highest sensitivity) were excluded from the analysis as these necessarily occurred when the ambient light level was high and anti-predatory flashes of bioluminescence would therefore be less likely to be detected. To remove the slowly varying direct current offset of the preamplifier, and aid detection of short transient flashes, a delaycorrected FIR high-pass filter (cut-off frequency 0. 5 Hz) was applied to the raw light level data. The GPS in the tag uses an infrared (940 nm) reflectometer to detect when the antenna is out of the water and this device produces a short (approximately 100 µs) flash of light every 101 ms, which is picked up by the bioluminescence sensor resulting in a stereotyped spike every five samples.
Solar and oceanographic data Solar angles were calculated for each day of the deployment using the first GPS position of the day, and a three-level day/night factor was associated with each PCA, flash and flash-PCA association, calculated as follows: day (sun angle >0 deg), twilight (sun angle 0-12 deg below horizon), night (sun angle >12 deg below horizon)  (Blanchet et al. , 2013). Twilight data (which comprise 12. 5% of the tag recordings) were included in total PCA counts but were not examined for flashes and flash-PCA associations because of the rapidly changing dive depths and surface illumination at these times. Sea surface temperature (SST) averaged over the study periods was obtained from AQUA-MODIS at 0. 1 deg spatial resolution. GPS and raster data were processed using QGIS v3. 6.
Field deployments Seven DTAGs with light sensors were deployed in October 2017 and 2018, only four of which were recovered as three seals moulted on inaccessible islands in the Kerguelen archipelago (Fig. 1 , Table  1 ). The tags recorded continuous high-resolution movement, location, audio and light data for 29-54 days. Excluding time spent in shallow water, a total of 102 days of data were available for analysis. The two individuals from KER spent most of their time in deep waters west of the Kerguelen shelf. One seal tagged at PV (PV18_1) headed east and crossed the continental shelf after 6 days to forage in deep, offshore waters. In contrast, the other individual (PV18_2) travelled south for 24 days over the shallow Patagonia shelf before reaching the continental slope off the Chilean coast, where it foraged in deeper waters for the remainder of the tag recording.
Bioluminescent flashes More than 2000 bioluminescent flashes were detected for each of the four animals, with flashes occurring in a large number of dives that were deep enough for the light sensor to attain its highest gain setting (Table  2 ). The number of flashes per day varied by individual from 72±42 (median±interquartile range, IQR) to 271±213, with relative flash intensities averaging 0. 9 and frequently overloading for KER17, and 0. 3 for the three individuals tagged in 2018. Notably higher flash rates and intensities were recorded by the tag on KER17, but this is unlikely to be due to a difference in sensitivity of this tag as the same unit was reused on KER18. We examined whether this strong difference could be related to the different software used in the two years; specifically, the tag in 2017 used an infrared LED to detect immersion and this produced stereotyped spikes in the light data which were removed in post-processing.
Flash and PCA associations Bioluminescent flashes were detected in close connection with PCAs (i. e. within 5 s of PCA start time) in all four datasets (Fig. 2 ). However, the occurrence rate of these compared with similar length intervals without PCAs was strongly significant (P<0. 01) in only 3 out of 4 individuals (Table  2 ). Within these three seals, the number of flashes associated with PCAs varied widely between individuals but only comprised >5% of all PCAs, and therefore a sizeable fraction of the diet, in the KER animals (Fig. 3 ). As a result, the relationship between flashes and PCAs was investigated further in these two animals only. The median duration of individual flashes associated with PCAs was 0. 46 s (IQR 0. 22-0. 86 s) for KER17 and 0. 22 s (IQR 0. 08-0. 44 s) for KER18.
SES range widely during their long foraging trips, taking very different routes even when starting from the same haul-out location  (Guinet et al. , 2014). Thus, individual animals encounter diverse prey resources, making it fraught to predict overall foraging behaviour from a small dataset. As in other studies, the SES tagged in this study encountered extensive prey resources arrayed in vertical layers that show predictable diel movements but less predictable horizontal structure. Our results demonstrate that SES can exploit large quantities of bioluminescent prey but the reliance on these varies widely, as shown by the contrast between the two study locations. The combination of wideband light and acceleration sensors in a tag therefore provides new insight into where and when different prey types are encountered, and how effectively they might be captured, meriting their use in more extensive studies of SES and other deep-diving apex predators.
Acknowledgements The Kerguelen fieldwork team 2017-2018 and 2018-2019 as well as the  Peninsula Valdes  fieldwork team 2018-2019 are gratefully acknowledged for deployment and recovery of tags, with special thanks to  R. Vera  and  M. Hindell in Peninsula Valdes , and  C. Vuillet ,  H. Hallegue  and  J. Mestre  at the  Kerguelen Islands. We thank  R. Swift  ( SMRU ) for help in building the tags,  L. Thompson  for insight into photodiodes and  C. A. Bost  ( CEBC-CNRS ) for loan of the Mk9. P. Couteille ,  S. Johnsen  and  I. Staniland  ( BAS ) provided insightful advice. We also thank two anonymous reviewers for their comments.
This work revises the concept of defects in crystalline solids and proposes a universal strategy for their characterization at the atomic scale using outlier detection based on statistical distances. The proposed strategy provides a generic measure that describes the distortion score of local atomic environments. This score facilitates automatic defect localization and enables a stratified description of defects, which allows to distinguish the zones with different levels of distortion within the structure. This work proposes applications for advanced materials modelling ranging from the surrogate concept for the energy per atom to the relevant information selection for evaluation of energy barriers from the mean force. Moreover, this concept can serve for design of robust interatomic machine learning potentials and high-throughput analysis of their databases.
MCD is an affine equivariant estimator, i. e. , the data might be rotated, translated or rescaled (e. g. , due to a change of the measurement units) without affecting the results  27. It is worth mentioning that MCD is tailor-made for unimodal distributions. Consequently, a careful selection of the training data should be performed (see Supplementary Note 2 for more details). The distortion score is computed for each atom in the analysed system via computing the statistical distance of the LAE with respect the learned distribution of the reference structure LAEs. The distortion score from MCD corresponds to the robust distance d RB (see "Methods", Eq. (  5 )). Figure  1b  shows the distortion scores computed for a simulation cell with 132 atoms, which contains four self-interstital atoms forming a threedimensional (3D) C15 cluster  38  in bcc Fe.
These configurations are included in the training database of the Gaussian Approximation Potential (GAP) for Fe  50. The atomic energies were computed using the same potential. The kernel formalism of GAP potential ensures the high accuracy of the atomic energy of the training configurations  50. For all three defect classes (Fig. 2 ), the determination-correlation coefficient R 2 between d RB and local energy is higher than 80%. The present approach completes the previous observation of Sharp et al. 2  in grain boundaries. The study 2 monitors the likelihood of atoms to rearrange within the grain boundaries through the so-called softness of atoms. The softness is a continuous, signed, scalar quantity that captures the relevant properties of the LAEs based on the binary classification using SVM.
For instance, in the case of widely used PW electronic structure calculations, the definition of energy per atom is ambiguous and requires to project delocalized electron density on local atomic orbitals. The definition of energy profile is critical in many statistical learning approaches, including QM/MM methods, which are currently at the forefront of computational materials science 4-6. are taken from the GAP potential database  50. The correlation is performed over 103,000 LAEs and each defect class gathers diverse instances from 0 K static relaxation to molecular dynamics simulations at various temperatures. MCD analysis is performed on the structural data represented using bispectrum SO(4)  36, 48  with the angular moment j max ¼ 4:5. The atomic energies are computed with the GAP potential  50.
Moreover, the automatic selection of relevant atoms can set the basis for finding appropriate collective variables, which is currently recognized as a critical problem that hinders implementation of free energy methods using automated and unsupervised simulation schemes  8, 10. Application 3: analysis of kernel ML potentials. Nowadays, ML force field models represent a worthwhile alternative to conventional interatomic potentials. The vast majority of existing ML force fields for MD calculations are based on kernel methods  11, 48, 55, 56. Accuracy and numerical cost of these potentials intrinsically depend on the diversity and number of LAEs M in the training database. The force fields built within the GAP framework  48  are among of the most commonly used ones. For the structures close to those from the potential database, GAP can be as accurate as ab initio methods  48, 50, 57.