dataesr/scientific-paragraphs-categorization huggingface dataset:
4 This analysis showed that the most enriched biological processes encompassing upregulated genes were response to stress, including the cellular response to oxidative stress and fungal cell wall organization (Table  2 ), whereas a large number of downregulated genes belonged to biological processes related to ribosomal biogenesis, translation and RNA processing (Table  3 ). Altogether, these data indicated that the 2-PE adapted C9 strain is set in a so-called stressing state. These data are supported by the finding that 93. 9% of the upregulated genes and 96. 4% of the downregulated genes in this C9 strain belong to the environmental stress response (ESR) reported in  Brauer et al. (2008)  (Figure  7 ). These transcriptomic data were also analyzed using the YEASTRACT database, 5  in particular to determine which transcription factors are implicated in the expression of enriched categories of upregulated and downregulated genes.
The functional category annotation and gene clustering were performed using the FunCat database. 10. 3389/fmicb. 2023. 1148065 Frontiers in Microbiology 11 frontiersin. org that belong to various cellular functions and pathways, which at first glance, were not related to 2-PE detoxification or metabolization. On the other hand, the transcriptome of this evolved strain was profoundly altered with more than 30% of all genes in the genome being differentially expressed as compared to the original strain. In addition, the transcriptomic profile of our 2-PE-adapted strain very much resembled those of yeast challenged with various stresses and leading to the so-called ESR-induced and repressed genes  (Gasch, 2000). These data strongly indicated that our C9 strain is poised in a permanent stressful state.
These events are then accompanied by uniform synthesis and strengthening Expression distribution of known environmental stress-response (ESR) gene groups in the 2-phenylethanol-resistant strain C9. The gray columns show the previously known genes that are expected to be repressed under ESR, while the black columns show the previously known genes that are expected to be induced under ESR, according to  Brauer et al. (2008). Percent contribution shows the percentage of significantly changed genes that has regulatory interaction with a given transcription factor according to the YEASTRACT database. of the stretched cell wall, thereby preventing potential cell lysis  (Mishra et al. , 2022). Interestingly, it was reported that this cell survival in response to a compressive mechanical stress was under the cooperative control of Pkc1p and calcineurin pathways, which are two major signaling pathways implicated in the cell wall compensatory response.
The strain wave is launched by the stress from electrons and phonons in Pt induced by the pump absorption by the electrons and the subsequent distribution of energy to the phonons (coupling time about 500 fs). From the initial state pictured in Fig. 4c , we observe three characteristic steps in the timedomain response: i) a compression of the NiO lattice up to 1. 5-2 ps after pump pulse arrival due to the ultrafast expansion of Pt lattice upon optical heating (Fig. 4d ). Then, we observe ii) a maximum expansion of NiO at 4 ps when the compressive part of the strain wave leaves NiO towards the substrate (Fig. 4e ) and finally iii) a quasi-static (very slowly relaxing) expansion of NiO after 6 ps. We then model the transient strain of NiO (blue line in Fig. 4b ) using a simple one temperature model (and the Python toolbox udkm1Dsim  38  ).
Acknowledgements E. R. ,  O. Gueckstock ,  H. J. ,  T. Kampfrath , M. K. , T. S. S. , S. D. and R. L. acknowledge financial support from the  Horizon   2020 Framework Program  of the  European Commission  under  FET-Open  grant agreement No. 863155  (s-Nebula). R. L. acknowledges financial support from the  Horizon   2020 Framework Program  of the  European Commission  under  FET-Open  grant agreement No. 964931  (TSAR). H. J. and R. L. acknowledge financial support from the  ANR  project  TRAPIST. M. Mattern  and M. B. acknowledge financial support from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) via No. BA 2281/11-1  Project-No. 328545488 -TRR 227 , project  A10. O. Gomonay , H. M. , C. S. , J. S. and M. K. acknowledge financial support by the  German Research Foundation DFG  ( CRC TRR 173  SPIN + X, projects  A01 ,  A03 ,  A11 ,  B02 , # 268565370 ) and TopDyn.
M. K. acknowledges financial support by the  DAAD (Spintronics Network  # 57334897  and  57524834 ) and the  Research Council of Norway (Center of Excellence 262633 "QSpin"). R. L. and M. K. acknowledge financial support from the  Horizon Europe Franework Program of the European Commission  ( HORIZON-CL4-2021-DIGITAL-EMERGING-01 ) under Grant Agreement 101070287 (SWAN-onchip). O. G and J. S. acknowledge financial support by the  German Research Foundation DFG  from the project  TRR 288 -422213477  (project  A09 ). T. Kampfrath and T. S. S. acknowledge financial support from the  German Research Foundation (DFG)  through the collaborative research center  SFB TRR 227 "Ultrafast spin dynamics " (Project  ID   328545488 , projects  A05  and  B02 ). T. Kikkawa and E. S. acknowledge financial support from  JST CREST  ( JPMJCR20C1  and  JPMJCR20T2 ),  JSPS KAKENHI  ( JP19H05600 ,  JP20H02599 ,  JP22H05114 , and  JP22K18686 ), and the  Institute for AI and Beyond of the University of Tokyo.
After vacuum-drying for 24 h, spectra were acquired using an UltrafleXtreme MALDI-TOF instrument (Bruker Daltonik GmbH, Bremen, Germany) equipped with a Smartbeam laser (Nd:YAG, 355 nm) monitored by the FlexControl software version 3. 4 (Bruker Daltonics, Bremen, Germany). MSI sequence was performed from whole ovarian section, targeting the regions with individual antral follicles. Spectra were acquired in the positive mode at a 2. 0 kHz laser repetition rate in the 200-2000 m/z range, using a spatial resolution set at 40 µm (medium focus setting). A total of 250 spectra per pixel were collected as a sum of 25 consecutive laser shots in 10 random walk steps. The unprocessed MSI sequences were then imported into the SCiLS Lab software (version 2016b, SCiLS, GmbH, Bremen, Germany). MSI sequences were loaded and pre-processed; data were baseline reduced with the setting of 20 for peak width using a convolution algorithm and normalized to the total ion count.
Then, the samples were incubated in PrOx/EPON epoxy resin mixture (Sigma-Aldrich, St Louis, MO, USA) in a 2:1 ratio for 2 h, in PrOx/EPON epoxy resin mixture in a 1:2 ratio for 2 h, with closed caps, and 16 h with open caps, and finally in 100% EPON resin for 24 h. Samples were included in fresh 100% EPON resin and incubated at 37 • C for 24 h and then at 60 • C for 48 h for polymerization. Ultrathin 70 nm sections were cut using "Leica Ultracut UCT" ultramicrotome (Leica Microsystems GmbH, Wien, Austria) and stained for 20 min with 5% uranyl acetate (Electron Microscopy Science, Hatfield, PA, USA), then 5 min with lead citrate, and finally placed on TEM nickel one-slot grids (Electron Microscopy Science, Hatfield, PA, USA), coated with Formvar film. The micrographs were obtained using JEM 1011 (JEOL, Tokyo, Japan) equipped with a Gatan digital camera driven by Digital Micrograph software (Gatan, Pleasanton, CA, USA) at 100 kV.
The matrix/sample mix was allowed to evaporate at room temperature for 15 min before MALDI-MS analysis. All spectra were acquired during the 30 min following the evaporation, using RapifleX MALDI Tissuetyper TOF mass spectrometer (Bruker Daltonics, Germany) equipped with a Smartbeam 3D Nd:YAG (355 mm) laser and FlexControl software (v4. 0). M5 defocus smart beam parameter was set at a 30 µm × 30 µm scan range with a resulting field size of 109 µm. Spectra were obtained in positive and negative reflectron ion mode in the m/z 100-1200 range at 5 kHz laser repetition rate, with a sampling rate of 1. 25 GS s -1. Ionization was achieved using a fixed laser power adjusted according to on-sample test shots in order to reach the optimal ionization threshold. Each spectrum was collected as a sum of 1500 laser shots in a random walk on spot.
Each sample was analyzed in triplicate. The parameters used for spectra acquisition were ion source 1, 19. 96 kV; Electrode P2, 1. 60 kV; lens, 8. 956 kV; and a pulsed ion extraction of 200 ns. The "Detector check" function was conducted in order to obtain a detector gain at appropriate voltages. For the measurements, the instrument was externally calibrated in Quadratic mode using a mixture of peptides and proteins. To increase mass accuracy (mass error < 0. 05%), internal calibration using a lock mass at m/z 760. 5851 (PC34:1) for positive ion mode and m/z 885. 5499 for negative ion mode was subsequently applied to all spectra, with flexAnalysis 4. 0 software (Bruker) and FlexAnalysis Batch Process (Compass 2. 0). All spectra were exported in mzxml format and treated using R software (version 3.
6. 1). Spectral processing and statistical analyses were performed using the MALDIquant and MALDIquantForeign packages (v1. 19. 3 and v0. 12) of the R software. The profile spectra were treated for baseline subtraction (SNIP method), smoothed by the Savitzky-Golay algorithm, and realigned using prominent peaks and normalization on intensity using the total ion count method. Peaks were detected in automatic mode using a total average spectrum. The precision of the acquisitions was determined by calculating the coefficient of variation (CV) from the normalized peak intensity values of the three technical replicates for each sample. Mean CV of the peaks was lower than 50%. As MS data did not pass the Kolmogorov-Smirnov test of normality, all lipidomic data were submitted to the non-parametric Wilcoxon test to identify changes between two conditions and to the Kruskal-Wallis test to identify changes between several conditions.
m/z were considered differentially abundant between the groups with a p-value < 0. 05. Hierarchical clustering (with Spearman correlations) and principal component analysis (PCA) were performed using the gplots (v3. 0. 3) and FactoMineR (v2. 1) packages of the R software and using XLSTAT Biomed 2018. 5 (Addinsoft, France).
Lipid Annotation The differential peaks were annotated using theoretical masses ± 0. 05% from the LIPID MAPS ® lipidomics gateway database considering [M + H]+ and salt adducts as [M + Na]+ or [M + K]+ in positive mode. For the negative mode, masses were annotated considering [M -H]-and chloride adducts as [M + Cl]-. The isotopes were characterized using enviPat package (v2. 4) of the R software. Follow-up annotation of m/z features from the MS spectra was based on lipid identifications obtained by high-resolution LC/MS on bovine follicular cells and fluids that were described earlier  [16]. For each detected ion, LIPID MAPS database was checked first, and then putative annotations were compared to the specific database of lipids identified from bovine FF and follicular cells  [16]. If the annotations overlapped, the lipid features from the specific database with the smallest m/z differences between observed and theoretical m/z were retained.
According to our data, the most representative lipid classes in cell-free FF were PC, SM, LPC, LPE, and PE, in corroboration with the previous lipidomic data on follicular cells and fluid  [16, 26]. Several of these lipids are usually associated with biological membranes and could be a part of "solid" FF fractions like apoptotic bodies and ffEVs (MVs and Exo), as detected in the EVs released by different human cells  [7]. We found that extracellular MVs and Exo showed significantly higher proportions of LPC and PI and a lower rate of SM compared to depleted FF. Although differences in lipid composition between MVs and Exo were evidenced, the most representative classes in both types of ffEVs were the PC, SM, LPC, PE, and PI, most of them being membrane building blocks. Cholesterol ester and ceramides were also detected.
Acknowledgments: The authors are grateful to the staff of the  PIXANIM platform (Phénotypage par Imagerie in/ex vivo de l'Animal à la Molécule)  for the use of mass spectrometers and software. We are grateful to the technical stuff of the research unit  PRC in INRAE Val-de-Loire Centre , notably  Albert Arnould , for collecting bovine ovaries. The assistance of the  IBiSA Electron Microscopy Facilities of Tours University and University Hospital  was greatly appreciated.
| Problem resolution The optimization is conducted thanks to a gradient descent algorithm structured on the model of the GRAPE algorithm. The algorithm is coded on Matlab (MathWorks Inc. , Natick, MA, USA; R2019b) using the solver of the function fmincon. The solver of this constrained nonlinear optimization uses a sequential quadratic programming (SQP) method. 13  At each iteration, an approximation of the Hessian matrix is computed using the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) method. 14  Analytic expression of the partial derivatives of objective and constraint functions is provided to the solver. To this end, it is necessary to differentiate the previously described steady state (Equation  12 ) with respect to the control field u: =ss ¼ A þ λh u ð Þ ð Þ 1 1 À λf u ð Þ ð Þ 2 =f u ð Þþ λ 1 À λf u ð Þ =h u ð Þ: ð22Þ Remembering Equation (  8 ), f u ð Þ¼ t e z Ue z and h u ð Þ¼ t e z Ue 0 , where e z ¼ t 0, 0,1, 0 ð Þand e 0 ¼ t 0,0, 0,1 ð Þ.
F I G U R E 1 4 MP-RAGE acquisitions with the optimal preparations focusing on different contrast objectives: (1) saturation of tube no. 12 and enhancement of tube no. 7, (2) saturation of tube no. 11 and enhancement of tube no. 7, and (3) saturation of tube no. 11 and enhancement of tube no. 9. In each case, line A displays the optimal 1p. -preparation for different segment durations from 1500 to 750 ms; line B displays the optimal 2p. -preparation for different segment durations from 1500 to 750 ms. The same window level is applied. MP-RAGE, magnetizationprepared rapid gradient-echo. The code has been pushed to a public git repository: https://github. com/vernier-alt/steady_state_article. git.
ACKNOWLEDGMENTS This work was performed within the framework of the  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). Experiments were carried out with the technical support of the staff of the radiology department at the  Centre Léon Bérard. A CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions.
A large range of climate change impacts is expected during the twenty-first century in vulnerable regions such as West Africa, where local populations largely rely on livestock systems as their main food production and income source. As climate change threatens livestock systems in various ways, here we assess how regional livestock could be exposed to cumulated climate-related stressors in the future. Using the world's largest multi-model climate impacts simulations database ISIMIP, we find that a large part of West Africa will experience at least 5-6 cumulated multiple climate stressors before the 2030s, including amplified severe heat stress conditions and flood risks. Consequently, about 30% of the current total regional livestock could be exposed to these cumulated stressors, with sheep and goat as the most affected species.
In this paper, the multi-model spread and global impact models (GIMs) vs Earth System Models (ESMs) uncertainties are also investigated by accounting for different GIMs and ESMs datasets for each climate indicator. Previous studies have shown that the majority of GIMs underestimate the extremeness of impacts in heat-related mortality, agriculture and terrestrial ecosystems, while impacts on water resources and hydropower are overestimated in some river basins both with a large spread among the models  32, 33. These combined uncertainties    S1  for models set for each indicator). from future scenario and multi-model must be taken into account when interpreting our cumulated multiple stressors, and our study here confirms the need for taking into account uncertainties and improving model representations for next generations of climate and impact models (CMIP6, ISIMIP3).
e. IPSL-CM5A-LR and HadGEM2-ES) and two low-climate sensitivity models (i. e. MIROC5 and GFDL-ESM2M). Three main Representative Concentration Pathways (RCPs) that illustrates possible greenhouse gases future scenarios are also available and include RCP2. 6, RCP6. 0 and RCP8. 5  20. In this paper, analyses are focused on the highest emissive RCP8. 5 greenhouse-gases scenario, since it provides the largest/clearest signal of the global warming on the multiple climate-related stressors we assess. Further details on ISIMIP2b specific protocol, data acquisition and bias-correction are provided in Frieler et al. 20  and Lange  52. Note that raw variables to calculate the eight indicators are not simulated by the same GIMs-ESMs combinations, resulting in a different total number of simulations for each indicator.
Detailed General Impact Models (GIMs) and Earth System Models (ESMs) combinations are displayed in Table  S1. Livestock data. The livestock exposure to multiple stressors is quantified using the Gridded Livestock of the World (GLW3  30  ) from the Food and Agriculture Organization (FAO). This dataset provides gridded data of absolute livestock quantity (i. e. number of heads) at a global scale for the year 2010 for eight main species: cattle, chicken, goat, sheep, pig, duck, horse and buffaloe (Figure  S1 ). Thought these data do not provide future projections of livestock, livestock dynamics and diurnal/seasonal displacements, it enables a robust assessment of a fixed livestock exposure to future cross-sectoral and combined stressors over Western Africa due to its high spatial resolution and multiple represented species.
According to ISIMIP2b data availability 58 , we similarly analyse the yearly lowest 2 % daily total (i. e. surface + subsurface) runoff of each year (values ≤ the 2th percentile), and here consider this metric as a low flow index and a drought risk indicator. In addition, the number of consecutive days with daily precipitation smaller than 3mm per year is quantified (here after consecutive dry days), to strenghten drought metrics analyses  59. Flood risk. Flood data strongly depend on the available data, which rely on local flood statistics, regional flood statistics, rainfall-runoff modelling, and local recommended choice  [60] [61] [62]. Flood studies usually require runoff data, river catchment data and case study of real flood events. These data are then compared to flood simulations to investigate needed conditions to exhibit possible flood event depending on regional conditions.
It results in the use of the daily maximum temperature (Tmax) instead of the daily mean temperature in THI calculation, similarly as previous studies  22, 63. Daily RH is usually derived from temperature and surface vapor pressure data in CMIP5 and ISIMIP2b, and we here consider the accuracy of the calculated RH at the moment of Tmax (i. e. RH max ) under the hypothesis that surface specific humidity does not signficantly change during the day  22. THI daily values can be calculated as: with THI the Temperature-Humidity Index  21, 22  , Tmax the daily maximum temperature, and RH max the cor- responding daily relative humidity when Tmax. Details of intermediate calculation steps are provided in Supplementary Materials. Cumulative approach of multiple stressors. To investigate how these various climate change indicators may cumulate in the future, each projected change is normalized and added to obtain a spatial distribution of combined climate stressors.
Acknowledgements A. B. was supported by both the  NERC/DFID   Future Climate For Africa programme  under the  AMMA-2050 Project , Grant Number  NE/M020126/1 , and by the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 869395. Data analyses benefited from the  IPSL Prodiguer-Ciclad  facility which is supported by  CNRS ,  UPMC ,  Labex  L-IPSL which is funded by the  ANR  (Grant  ANR-10-LABX-0018 ) and by the  European FP7   IS-ENES2  project (Grant  312979 ).
fMRI acquisition. Prior to the main experiment, the EEG electrodes were removed from the 6 implanted birds, and the photoperiod was inverted to facilitate sleep recordings during the daytime. As pigeons, like diurnal mammals, engage more frequently in REM sleep later in the night  50  , the fMRI recordings were performed in the second part of the birds' subjective night. All MRI data collection was carried out in a Bruker BioSpec 7 Tesla scanner (horizontal bore, 70/30 USR, Avance III electronic, Germany) using Paravision 6. 0 software. A quadrature birdcage resonator (82 mm ID) was used for RF transmission and a single-loop receiver surface coil (20 mm ID) was used for restingstate and anatomical data collection. By positioning the ring surface coil around the head, it was possible to reduce artifacts due to body movements.
As changes in respiration may affect BOLD signals and brain connectivity maps, the respiration waveform was measured using a small pneumatic pillow placed under the pigeon's chest muscles (Small Animal Instruments, Inc. Model 1025T monitoring and gating system) during all resting-state measurements. The facial behavior of the animals was simultaneously recorded using an MRI-compatible video camera (12M-i, MRC Systems, Heidelberg, Germany; B/W) with an incorporated LED light (IR) (outside the pigeon's visible spectrum). To facilitate detection of changes in pupil size and eye position, a small mirror was placed next to each eye (Fig. 1A ). The videos were acquired at 30 fps. All recordings including respiration signals, videos, and resting-state data were synchronized using a TTL signal. To localize the correct position of the pigeon brain within the scanner bore, a series of scout images were measured at the beginning of each scanning session.
Three runs (horizontal, coronal, and sagittal) were acquired using multi-slice rapid acquisition (RARE) with the following parameters: TR = 4 s, TEeff = 40. 37 ms, RARE factor = 8, no average, acquisition matrix = 128 × 128, FOV = 32 × 32 mm, spatial resolution = 0. 25 × 0. 25 mm 2 , slice thickness = 1 mm, number of slices = 20 horizontal, 17 sagittal, and 15 coronal. These images were used to orient 11 coronal slices to cover the entire telencephalon, optic lobes, midbrain, 4 th ventricle, and cerebellum. Resting-State fMRI (rs-fMRI) data were acquired using a single-shot multi-slice RARE sequence adapted from Behroozi et al. 18, 47  , with the following parameters: TR = 4000 ms, TEeff = 41. 58 ms, partial Fourier transform accelerator = 1. 53, encoding matrix = 64 × 42, acquisition matrix = 64 × 64, FOV = 30 × 30 mm 2 , in-plane spatial resolution = 0.
47 × 0. 47 mm 2 , radio-frequency pulse ip angles for excitation and refocusing = 90°/180°, slice thickness = 1 mm, no slice distance, slice order = interleaved, excitation and refocusing pulse form = scanner vendor gauss512, receiver bandwidth = 50,000 Hz. To saturate the signals from the eyes to avoid brain image corruption due to eye movements, two saturation slices were positioned manually over the eyes. Each run of the rsfMRI recordings included 1000 to 1450 volumes. To check the reproducibility and stability of the results, rsfMRI data of all animals were recorded twice on different days. High-resolution T2-weighted anatomical images were acquired using a RARE sequence for better spatial normalization. Scan parameters were as follows: TR = 2000 ms, TEeff = 50. 72 ms, RARE factor = 16, number of averages = 1, FOV = 25 × 25 × 15 mm 3 , matrix size = 128 × 128 × 64, spatial resolution = 0.