dataesr/scientific-paragraphs-categorization huggingface dataset:
For the label-free quantitation results, peptide quality score was set to be ≥4 and protein significance score threshold was set to 20. The significance score was calculated as the -10log10 of the significance testing p-value (0. 01). ANOVA was used as the significance testing method. The minimum number of peptides from a protein to be identified per condition was set to 2 and only peptides present in at least half of the samples from the same condition were considered in the analysis. Total ion current was used to calculate the normalization factors. At a threshold of 2. 5 the same proteins were consistently overrepresented at the different timepoints, highlighting the significance of the specific biological processes uncovered. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  65  partner repository with the dataset identifier PXD043051 and 10.
Sequences were then aligned using mafft  67  with default parameters and the resulting alignment was inspected in order to manually fuse back non-overlapping hits from a given protein. The aligned bdelloid sequences were kept for later use. AvLigE-B sequence was also used to perform a diamond blastp search  68  against uniref50 database using the following parameters: -more-sensitive -k 20000 -max-hsps 1 -evalue 1e-05 -query-cover 20. A total of 502 matched subject sequences were retrieved from the Uniref50 database, out of which only the best 155 hits were kept for subsequent analysis. These sequences were added to the bdelloid sequences previously retrieved as well as to sequences corresponding to ligases 1, 3, 4, B, C, D and K retrieved from NCBI (see Supplementary Table  S3  for a list of accession numbers used in this analysis).
These steps resulted in the production of two datasets: one comprising 543 sequences (502 blast hits combined with bdelloid sequences and other ligases) and one comprising 184 sequences (155 best blast hits combined with bdelloid sequences and other ligases). Both datasets were then aligned using mafft with default parameters  67. A third dataset based on the 184 sequence alignment was produced by only retaining both ATP and OB ligase proteic domains from the full ligase sequences. For the three datasets, alignment columns for which more than 80% missing data was observed were discarded using custom perl script. Considering the 184 sequence alignments, this reduced its length from 4935 aa to 529 aa. The reduced alignment was used as input for a phylogenetic analysis under maximum likelihood using iq-tree 1.
To compare the relative abundance of the DNA ligase transcripts between irradiated and non-irradiated conditions, the real-time quantitative PCR experiments were performed with primers targeting the gapdh gene (=housekeeping gene) or the specific DNA ligase gene of each organism. All primer designs were performed using the online ePrimer3 software using the standard settings. The oligonucleotides used in this study are listed in Supplementary Table  S4. Relative gene expression data was determined using the Livak method  74.
Proliferation potential in each condition was measured by reporting the mean number of colonies growing at 0. 002 and 0. 004 kGy of X-rays to the mean number of colonies growing in the non-irradiated control. The experiment was performed in two biological replicates, each including three technical replicates (n = 6). Statistical significance between the control cell line (HEK SCR) and the cell lines expressing AvLigE-A/B under the variable irradiation conditions was determined by multiple ttests using the Holm-Sidak method, with alpha = 0. 05 using Prism 7 software.
Data availability Raw data are available in the Source Data file related to this manuscript. Protein sequences used in this study are available in the public repository NCBI NCBI, assembly ASM2161353v1. Mass spectrometry raw and processed data have been submitted to the PRIDE (Proteomics Identifications Database) repository with the dataset identifier PXD043051. Datasets used for phylogenetic analyses are available on Figshare: https://figshare. com/s/7cbed8c5d59abdd2f4ae. Data presented in this manuscript are protected under the patent WO 2023/ 161266 Methods and Compositions for increasing stress tolerance of cells and organisms (wipo. int). Source data are provided with this paper.
Acknowledgements The authors thank  Catherine Demazy  and  Anne-Catherine Wéra  for their generous help during the course of this study. The Morphim imaging platform and the  Research Unit  for Analysis by  Nuclear Reactions (LARN) of UNamur  are acknowledged for their technical help. This work was supported by the  European Research Council (ERC)  grant agreement  725998  (RHEA) to K. V. D. , and from the  Fédération Wallonie-Bruxelles  via an' Action de Recherche Concertée' (ARC)  grant to K. V. D. and B. H. E. N. obtained a  FSR UNamur  fund.
The Compton Spectrometer and Imager (COSI) is a selected Small Explorer (SMEX) mission launching in 2027. It consists of a large field-of-view Compton telescope that will probe with increased sensitivity the under-explored MeV gamma-ray sky (0. 2-5 MeV). We will present the current status of cosipy, a Python library that will perform spectral and polarization fits, image deconvolution, and all high-level analysis tasks required by COSI's broad science goals: uncovering the origin of the Galactic positrons, mapping the sites of Galactic nucleosynthesis, improving our models of the jet and emission mechanism of gamma-ray bursts (GRBs) and active galactic nuclei (AGNs), and detecting and localizing gravitational wave and neutrino sources. The cosipy library builds on the experience gained during the COSI balloon campaigns and will bring the analysis of data in the Compton regime to a modern open-source likelihood-based code, capable of performing coherent joint fits with other instruments using the Multi-Mission Maximum Likelihood framework (3ML).
Overview The Compton Spectrometer and Imager (COSI)  [1]  is a compact Compton telescope sensitive to gamma rays between 200 keV and 5 MeV. COSI is a survey instrument with a large field of view -> 𝜋 sr-excellent energy resolution -<1. 2% (<0. 07%) at 511 keV (1. 157 MeV)-capable of imaging with a good angular resolution -< 4. 1 • (< 2. 1 • ) at 511 keV (1. 809 MeV)-and performing polarization measurements. COSI will reduce the so-called "MeV gap", the low-energy part of the gamma-ray spectrum that despite its great scientific potential is currently under explored. COSI will uncover the origin of the Galactic positrons, advance our understanding of how the elements were formed, and probe the physics of extreme environments and multi-messenger sources. The COSI team is currently developing cosipy, a Python library that will be capable of performing all the high-level task expected for COSI: imaging, spectral analysis and polarimetry.
The cosipy library Although the basic principles of Compton data analysis can be applied to approximate the spectrum, location and polarization of source, real data presents significantly more challenges. The large number of events, high and variable backgrounds, instrument artifacts, and the continuous rotation of the spacecraft, all call for a more complex and robust high-analysis software. The timedependent spectra and polarization, and correlations between energy, polarization and direction, also complicate the analysis. The cosipy library is a component of a series of software packages called cositools, and the last step of the COSI calibration and analysis pipeline, shown in 5. It builds upon the development during the COSI balloon campaigns. For a full description of the calibration and pipelines see  [2, 3].
, 1992)  which opens a wide range of further calculations, including details about precipitation in high mountains regions  (Sakai et al. , 2015). And, most importantly, glacier outlines spatially constrain glacier-specific calculations such as elevation changes, flow velocities and debris-covered fractions  (Scherler et al. , 2018). For the former two, glacier outlines also constrain where "off glacier" terrain is. These stable regions can be used for several steps in the data processing (e. g. DEM co-registration) and for the uncertainty assessment (e. g. ,  Nuth and Kääb, 2011). In short, glacier outlines are required for nearly all glacier-related calculations  (Kargel et al. , 2005). But how are glacier outlines created? Nowadays, scientists use inventories readily available, most frequently from the Global Land Ice Measurements from Space (GLIMS) glacier database (which is multi-temporal) or the Randolph Glacier Inventory (RGI), which provides a single snapshot of glacier extents and is available in a globally near complete version since 2011  (Pfeffer et al.
, 2014). Since then, the RGI has been continuously improved and is now available in its version 6  (RGI Consortium, 2017). Outlines available in the GLIMS database and from the RGI have been freely provided by the scientific community on a voluntary basis as an outcome or by-product of diverse research projects. This means that updating and filling of the database is slow and is so far uncoordinated. Today, a working group 1 of the International Association of Cryospheric Sciences (IACS) is coordinating glacier data compilation and redistribution. The group also prepares the release of RGI version 7. As glacier outlines are mostly derived from optical satellite images, the opening of the Landsat archive in 2008  (Wulder et al. , 2012) , with free access to orthorectified imagery, started a new era in global glacier mapping and change assessment.
, 2016). The resulting binary image is transformed to polygons using raster to vector conversion (Figure  3 ). Remaining omission (ice hidden by clouds or debris cover) and commission errors (water, bare rock, seasonal snow, lake and river ice) have to be manually corrected during post-processing. In regions with many debris-covered glaciers, this last step is very laborious and time consuming  (Racoviteanu et al. , 2022). Debris cover on glaciers is thus the bottleneck of global glacier mapping efforts and hinders rapid and easy updates of the global datasets with its >200'000 glaciers. Once glacier outlines with debris cover are mapped, the extent of debris cover can be determined as a residual from automated clean ice mapping  (Scherler et al. , 2018; Herreid and Pellicciotti, 2020). Other problematic factors are remaining seasonal snow and clouds hiding the glacier perimeter.
, 2014). Since April 2016, ASTER images have been freely available which boosted their use by the scientific community. As of writing, ASTER is still acquiring stereo pairs but the end of the mission is planned for September 2023 and, unfortunately, ASTER will not be replaced. The  SPOT5-HRS archive (2002 -2015)  has been publicly opened recently (June 2021) as part of the SPOT World Heritage program (https://regards. cnes. fr/user/swh/) from the French Space Agency, CNES. Before that, access to the images was restricted to the French mapping agency and the French defence. Only during the international polar year (IPY), 2007-2009, a dedicated image acquisition program took place over the polar regions. The SPIRIT (Spot5 stereoscopic survey of Polar Ice: Reference Images & Topographies) initiative distributed 40-m DEMs and 5m ortho-images of glaciers and ice sheet margins to the scientific communities  (Korona et al.
The same also applies to Cartosat-1  (Pieczonka et al. , 2011). Various software has been used to generate DEMs from the stereo-pairs. Up to a few years ago, most of the processing was done using commercial software such as PCI Geomatica  (Toutin, 2001)  or ERDAS  (Bolch et al. , 2008). Recently, open-source tools such as MicMac, SETSM or ASP have been developed to process high resolution images  (Beyer et al. , 2018; Rupnik et al. , 2018; Noh and Howat, 2015)  and were also adapted to medium resolution images  (Girod et al. , 2017; Shean et al. , 2020). (Hugonnet et al. , 2021) and (d)  during the 20 year period from 1 January 2000 to 31 December 2019  (Hugonnet et al. , 2021). The black polygon is the RGIv6 outline, derived from 1999-2003 imagery according to the RGI handbook, although these elevation change maps suggest that the outlines were derived from older imagery.
Level 3B gridded ice height and height change data (ATL14, ATL15) were made public in late 2021. The 1064 nm laser on board ICESat led to little multiple scattering within snow and ice and therefore no elevation bias  (Smith et al. , 2018). ICESat-2 uses a 532 nm laser that, under certain conditions, can experience a large degree of multiple scattering within the snow and ice. The magnitude of this elevation bias depends on snow conditions and the method of identifying the surface height from the photon cloud  (Smith et al. , 2018). It can be as large as 24 cm in extreme cases (e. g. pure snow/ice with extremely large grains). However, for most applications, the bias is negligible (2-5 cm) and largely cancels for repeat measurements under similar snow conditions.
Principles to assess glacier changes ICESat was only capable of pointing to the same location on the ground (i. e. reference ground track) to within 10s to 100s of metres and measurements were made every ca. 170 m alongtrack. This makes it challenging to directly compare near-repeat elevation measurements, as elevation changes will be convolved with differences in elevation due to changes in measurement location (Figure  7 ). This is often referred to as the "slope error" and must be corrected for. ICESat-2 has far superior pointing control (3-25 m) and closely spaced beam pairs (90 m) that make correcting for "slope error" robust and straightforward. In steeper or rougher terrain or in the mid-latitudes without quasi-exact repeat tracks, different ground tracks may not be directly comparable altogether without an additional reference elevation dataset and the use of spatial statistics (Figure  7 ).
, 2008). Most variants of this method assume that the surface slope and rate of elevation change are constant over several years, though higher order models can also be fitted  (Nilsson et al. , 2016). This approach can be sensitive to how the tracks are ordered in space. Schenk and Csatho (2012)  found that higher-order fitting surfaces better captured the more complicated thinning/thickening signal in the rapidly changing coastal areas of the ice sheets. A third method relies on a DEM to estimate, and correct for, the terrain differences between repeat ground tracks  (Slobbe et al. , 2008). A variant of this last method is also applicable to non-repeat measurements and is discussed in section 5. 2. 2. All of the above methods are most applicable to ICESat data and less relevant to ICESat-2 data, when ICESat-2 data is acquired along repeated reference ground tracks.
The result is a single dh/dt estimate for the analysed group of elevation samples. In the case of ICESat, given the sparse ground tracks, this is often a rather large area such as an entire mountain range. There are some caveats with this method: (1) the elevation differences to the reference DEM (dh) contain both real elevation changes between the DEM and laser altimetry data as well as uncertainties and potential bias in either the DEM, the laser altimeter data, or both; and (2) dh have to be binned spatially and filtered to remove outliers and make sure that they are representative for the glaciers in the sampled area. Careful preprocessing is vital to reach reliable results, in particular a robust assessment of biases and uncertainties in the reference DEM, elevation biases from snow cover during ICESat/ICESat-2 acquisitions, and the elevation distribution of the laser altimetry samples  (Treichler et al.
, 2019). This approach has been applied to the larger glacierized regions across the globe and in particular to High Mountain Asia, where glacier changes were poorly known before ICESat data became available  (Farinotti et al. , 2015; Treichler et al. , 2019; Gardner et al. , 2013; Kääb et al. , 2012). The increased number of measurements provided by ICESat-2, relative to ICESat, significantly augment the utility and accuracy of this approach.
Outlook New methods take time to develop for new products like ICESat-2, and as of this writing, there are relatively few results in the literature. It is expected that ICESat-2 data will contribute to new insights and a much better understanding of the entire cryosphere, including dynamical changes of the ice sheets  (Smith et al. , 2020)  and improved monitoring of mountain glaciers  (Wang et al. , 2021; Fan et al. , 2022) , but also advances in related disciplines such as, snow depths on-and off-glacier  (Treichler and Kääb, 2017) , sea ice and ice shelf thickness  (Kacimi and Kwok, 2020)  or permafrost changes  (Michaelides et al. , 2021). In addition, the accurate elevation measurements of both ICESat and ICESat-2 provide a globally consistent reference elevation dataset that has the potential to greatly improve the quality and accuracy of existing and upcoming DEMs and other elevation data across the globe, which will directly benefit glacier volume change studies.
Using an appropriate scaling (see  Wahr et al. , 1998 for details) , variations in the geopotential can be expressed as variations in surface water loading. This is usually expressed in units of metre water equivalent height. It should be kept in mind that GRACE can only measure anomalies in surface water loading, not the absolute weight of a glacier or ice sheet. GRACE data are distributed by the GRACE science data centres, and other parties, as spherical harmonics (the C lm and S lm coefficients in the equation above). Since higher-order coefficients are increasingly affected by observational noise, only coefficients up to degrees and orders smaller than 60 or 90 are typically used in the derivation of the surface water loading anomalies, which corresponds to a maximum spatial resolution of approximately 200-300 km.
Elevation anomalies are determined relative to the ArcticDEM 8-m Mosaicked DEM v3  (Porter et al. , 2018). Despite a rather scarce spatial sampling, laser altimetry is able to capture the main patterns of change, with thinning in the lower reaches and slight thickening in the accumulation area of the two largest outlets of northern Vatnajökull (Dyngjujökull and Brúarjökull). However, ICESat/ICESat-2 has no measurement below 450 m a. s. l. and misses the very strong thinning rate on the tongues of the south and south-east flowing outlets. Given this sparse spatial sampling of a complex pattern of elevation changes, we did not attempt here to estimate the total volume and mass change of Vatnajökull. Hence, laser altimetry data are not plotted in Figure  11  and not listed in Table  2. Due to their coarse spatial resolution, gravimetric observations are not able to resolve Vatnajökull mass change alone (Figure  10d ).
There are large data gaps in the elevation change maps due to the lack of contrast, mostly in the upper accumulation areas. This is particularly the case when only two DEMs are compared (Fig. 12a ). These gaps are reduced when processing a time series of ASTER DEMs (Fig  12b ). Gaps would almost vanish if DEMs were derived from 11 or 12-bits modern stereo sensors as illustrated by the complete map of elevation differences for Mera Glacier (30 km south of Mt Everest) obtained by comparing Pléiades DEMs from 2012 and 2018  (Wagnon et al. , 2021). Altimetry data, from ICESat and CryoSat-2, are not able to resolve individual glaciers. They are only able to provide a region-wide mass balance estimate when aggregated over sufficiently large regions so that glaciers (in particular the different elevations bands) are well sampled.
There are currently no funded laser altimetry missions under development. To our knowledge, no satellite mission is funded to replace ASTER and SPOT5-HRS in order to provide a global, frequent and open-access coverage of the continents with optical stereo-images. A successor should be considered rapidly. Meanwhile, releasing the stereo-images from past or ongoing commercial and defence satellites for scientific use would help to mitigate the data gaps. Would it not make sense in view of the climate crisis?.
Acknowledgments We dedicate this article to our colleague  Jérémie Mouginot  ( CNRS Glaciologist at IGE, Grenoble ), who left us in September 2022, far too early. Anny Cazenave  and  Jérôme Benveniste  stimulated us to write this review. We also thank  Maud Bernat  and  Jérôme Lebreton  for commenting on an earlier version of the manuscript. Comments by two anonymous referees contributed to improve the article. Etienne Berthier  acknowledges support from the  France Space Agency (CNES)  through the  TOSCA and Pléiades Glacier Observatory  programs. The contribution of  Frank Paul ,  Désirée Treichler  and  Andreas Kääb  was supported by funding from the  ESA  Project  Glaciers_cci+  ( 4000127593/19/I-NB ) and in addition for  A. K. ESA Harmony  ( 4000135083/21/NL/FF/ab ) and for D. T. Research Council of Norway  (SNOWDEPTH, contract  325519 ).
Noel Gourmelen  acknowledges support from the  European Space Agency  for the development and application of CryoSat-2 swath processing via Projects CryoTop ( 4000107394/12/I-NB ),  CryoTop Evolution  ( 4000116874/16/I-NB ),  CS+ Mountain Glaciers  ( 4000114224/15/I-SBo ), and  CryoTEMPO EOLIS  ( 4000128095/19/I-DT ). Bert Wouters was funded by  NWO VIDI  Grant  016. Vidi. 171. 063. Dana Floricioiu  and  Lukas Krieger  were supported by the  DLR  project  'Polar Monitor'. Alex Gardner  and  Amaury Dehecq  were funded by  NASA's Cryospheric Sciences and MEaSUReS Programs. This is a contribution of the  RAGMAC (Regional Assessments of Glacier Mass Change) working group of the International Association of Cryospheric Sciences, and of the Glacier Mass Balance Intercomparison Exercise (GlaMBIE)  project of the  European Space Agency  ( 4000138018/22/I-DT ).
Once the animals reached a state of stable weight, we conducted evaluations of compulsive overeating and behavioral in exibility through dedicated stand-alone tests. These tests allowed us to gain insights into the presence of these behaviors in the mice. Finally, mice were euthanized with CO 2 and the monoamine content in striatum (ST) and PFC was measured (Fig. 1a ). Feeding behavior analysis. We used the PheCOMP multi-take metabolism cages (Panlab), that provide ne-grain feeding behavior data for detailed meal pattern analysis. Two food dispensers containing SC and CM or HFD were located in the PheCOMP cages, counterbalanced between cages and genotypes (for a complete description, see  [22]. The meal pattern was analyzed using the COMPULSE software (Panlab). We analyzed the number and average duration of meals, the total food consumed, and eating rate (food consumed per second).
We calculated the food/energy intake, the circadian and ultradian frequency distributions of meals, and the satiety ratio (intermeal intervals divided by the amount (g) of food consumed in the preceding meal). To assess potential pellet loss during the course of the experiment, we developed a custom-made R code speci cally designed for this purpose (available upon request). In cases where pellet loss was detected, appropriate corrections were made to the data to ensure accuracy and reliability. Limited access to energy-dense foods. Access to CM or HFD was restricted to 1h during the light (resting) phase of the light-dark cycle (from 14:00 to 15:00h) for 3 consecutive days in overweight mice (SC + CM and SC + HFD) to analyze in exibility and binge-like eating episodes. In exibility refers to the inability to adapt to new or changing events or situations.