dataesr/scientific-paragraphs-categorization huggingface dataset:
Export the frequency using the Table function of FlowJo. 8. In Excel, correct for the signal of the isotype control by subtracting the percentage of positive cells in the T cell controls stained with isotypes from each of the samples stained with antibodies against the different cytokines. 9. The results of proliferation as well as cytokine production of the T cells can now be plotted using GraphPad Prism or any other preferred software (see Fig. 32  as an example). Summarized results are shown in Table  64. In the positive gates, less than 1% positive cells should be contained for the isotype controls. 16. Apply the gating for the cytokines to all samples and add "Frequency of grandparents" or "Frequency of parents" to each of the gated cytokine + populations. Export the frequency using the Table function of FlowJo.
17. In Excel, correct for the signal of the isotype control by subtracting the percentage of positive cells in the T cell controls stained with isotypes from each of the samples stained with antibodies against the different cytokines. 18. The results of proliferation as well as cytokine production of the T cells can now be plotted using GraphPad Prism or any other preferred software (see Fig. 34  as an example). Summarized results are shown in Table  64.
If desired, tracks with a distinct track length can be specifically selected for downstream analysis by adjusting the parameters for 'Number of slices' accordingly. Add the dataset and apply the settings. Once initialized, tracks can be plotted, for example, on an X/Y diagram or as rose plot. For obtaining additional parameters such as directionality, velocity, accumulated or Euclidian distance, select the 'Statistic feature' tab. 2. To analyze the cell shape, the 'Particle Analyzer' can be employed: Open an 8-bit greyscale image of a fluorescent staining visualizing the entire cell. Select Image>Adjust>Threshold. Thereby, the image is converted into a binary image. Make sure that "Dark background" is selected. If necessary, adjust the threshold manually using the scroll bars until the binary image is representative of the original greyscale image and press "Set".
Sections 1, 9, 12 and 13: The Dudziak laboratory was supported by the German Research Foundation [Deutsche Forschungsgemeinschaft (DFG)] (CRC1181-TPA7 261193037, DU548/5-1 420943261, TRR305-TPB5 429280966, RTG2504 401821119, RTG2599 421758891), the Interdisziplinäres Zentrum für klinische Forschung (IZKF) (IZKF-A80, IZKF-A87) and the Bavarian State Ministry of Science and Art (Bayresq. Net-IRIS). Diana Dudziak was funded by the Agence Nationale de la Recherche (ANR) and the DFG (DU548/6-1 431402787). Section 2: Dr. Luciana Berod received intramural funding from ReALity, a joint initiative of all principal investigators of the Faculty of Biology, the Focus Program Translational Neurosciences (FTN), the Research Center for Immunotherapy (FZI), and the Center for Translational Vascular Biology (CTVB) at Johannes Gutenberg University Mainz.
Acknowledgments. LMU Institutional Strategy LMU-Excellent  within the framework of the  German Excellence Initiative  (to J. R). Section 7: This work was supported by the  Human Frontier Science Program (HFSP)   RGP0032-2022 ,  Deutsche Forschungsgemeinschaft (DFG)  grant number  335447717-SFB 1328  (project  A20 ), and  Forschungszentrum Medizintechnik Hamburg (FMTHH)   04fmthh2021  to P. J. S. Section 8: We thank members of the  Clausen laboratory  for fruitful discussions and critical reading of the manuscript. Work in the Clausen laboratory  is supported by grants from the  German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)  to  B. E. C. ( CL 419/2-2  [Project Nr. 315501751],  CL 419/4-1 ,  CL 419/7-1  [Project Nr. 503972215], and  SFB1292/2 TP20N  [Project Nr. 318346496 ]) and  R.
A. B. ( BA 5939/2-1 ). B. E. C. and R. A. B. are members of the  Research Center for Immunotherapy (Forschungszentrum Immuntherapie, FZI) of the University Medical Center Mainz. Section 10: This work was supported by the  German Research Foundation  (DFG): Ma1924/9-2 Section 11: This work was funded by  Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy   EXC2151  (Project  390873048 ) to  S. B. Open  access funding enabled and organized by Projekt DEAL.
Acknowledgments: Sections 1, 9, 12 and 13: The Dudziak laboratory was supported by the German Research Foundation [Deutsche Forschungsgemeinschaft (DFG)] (CRC1181-TPA7 261193037, DU548/5-1 420943261, TRR305-TPB5 429280966, RTG2504 401821119, RTG2599 421758891), the Interdisziplinäres Zentrum für klinische Forschung (IZKF) (IZKF-A80, IZKF-A87) and the Bavarian State Ministry of Science and Art (Bayresq. Net-IRIS). Diana Dudziak was funded by the Agence Nationale de la Recherche (ANR) and the DFG (DU548/6-1 431402787). Section 2: Dr. Luciana Berod received intramural funding from ReALity, a joint initiative of all principal investigators of the Faculty of Biology, the Focus Program Translational Neurosciences (FTN), the Research Center for Immunotherapy (FZI), and the Center for Translational Vascular Biology (CTVB) at Johannes Gutenberg University Mainz.
Thus, the invariance property gives access to multiple virtual operators {A g } g=1,. ,|G|. For a given measurement y, each operator A g might have a different submanifold of possible inverses A -1 g (y). Thus, it is feasible to learn f θ from only measurements if the intersection of these submanifolds g A -1 g (y) contains a single signal x. The invariance property also tells us that the composition f θ • A should be equivariant to the transformations (see Figure  2 ): T g f θ (A(x)) = f θ (A(T g x)) (3) for g = 1,. , |G|. To exploit this idea the EI training strategy enforces both measurement consistency A(f θ (y)) = y and the equivariance condition in (3) using a dataset of measurements {y i } i=1,. ,N. The reconstruction function f θ is then learned by minimizing the following loss: L EI (θ) = L MC (θ) + αL EQ (θ), (4) with L MC (θ) = N i=1 1 m ∥y i -A (f θ (y i )) ∥ 2 , (5) L EQ (θ) = N i=1 |G| g=1 1 |G|n ∥T g f θ (y i ) -f θ (A(T g f θ (y i ))) ∥ 2 (6) where the first term (5) enforces measurement consistency (MC) and the second term (6) enforces system equivariance, and α is a trade-off parameter.
Self-supervised denoising via SURE In most real-world settings, observed measurements are corrupted by noise. For example, measurements can contain Gaussian noise, that is y|u ∼ N (u, Iσ 2 ), (7) where u denotes the clean measurements, σ is the standard deviation of the noise and I denotes the identity matrix. A denoiser network h θ that maps y → u is generally trained with a dataset of noisy and clean pairs (y, u) via the supervised loss L MSE (θ) = 1 m N i=1 ∥y i -h θ (u i )∥ 2 2. (8) However, in practice sometimes only the noisy observations y i are available. In settings where the noise distribution is Gaussian, it is possible to bypass the need of clean training data with a SURE-based loss  [38]  L SURE (θ) = N i=1 1 m ∥y i -h θ (y i )∥ 2 -σ 2 + 2σ 2 m ∇ • h θ (y i ) (9) where ∇ • h θ (y) = m j=1 ∂h θ,j (y) ∂yj is the divergence of the function h θ with respect to the noisy input y.
In this task, we exploit the invariance of MRI images to rotations, and use rotations of integer degree (|G|=360), while α is set to one (see SM for the effect of varying α). The MRI data used were obtained from NYU fastMRI Initiative  [40]. We trained and tested on a subset of the single-coil measurements at 4× acceleration. There are 973 samples in the dataset, the first 900 were used for training and the last 73 were used for testing. Each image is of size 320 × 320 pixels. In all tests, we use complex-valued data treating the real and imaginary parts of the images as separate channels. For the purpose of visualization, we display only the magnitude images. The reconstruction results are reported in Table  1  and Figures  4 5. Note that EI only performs well for small noise levels, e. g.
For A † we use the filtered backprojection (FBP), iradon, of the logarithm of the measurements, log(I 0 /y). The reconstruction function is thus defined as f θ (y) = G θ • iradon • log(I 0 /y). We therefore use the MPG model, (22) to implement (14) and train the REI models. We use the CT100 clinical dataset  [5] , which comprises 100 real in-vivo CT images collected from the cancer imaging archive. The CT images were resized to 256 × 256 pixels. The radon function consists of 50 uniformly sampled views (angles). For the imaging noise we follow [7] and generate MPG noisy measurements with I 0 = 10 5 , σ = 30 and γ = 1. We used the sinograms of the first 90 images for training and the remaining 10 for testing. In this task, we use the U-Net which is the same architecture as FBPConvnet in  [17].
Implementation This section presents the common material for our experiments. To illustrate the interest of representation, the basic and improved training sets are made from the same vec-  tor data, that cover an area of 30* 15 kilometres in the southwest of France. A detailed database for maps at the 1:25,000 scale of roads, water features and buildings, and their generalisation for cartography at the 1:50,000 scale. There is also a layer of the city centre that needs to be grayed at the generalised scale  (Touya and Dumont, 2017). To evaluate the success of learning, without real formalisation of the evaluation process for maps generated using deep learning  (Courtial et al. , 2020b) , we decided to only evaluate visually the results for each task. Our visual evaluation is guided by the following constraints for a generalised topographic map  (Courtial et al.
, 2021b)   This map generation task involves a GAN  (Isola et al. , 2018 ) that aims at the generation of a new image that looks like the target images from our training set. This architecture combines a generator that creates the image and a discriminator that evaluates if the generated image looks like the target domain. The generator for map generation with-out additional information is a U-Net  (Ronneberger et al. , 2015)  while the experiments on additional information use a FuseNet  (Hazırbaş et al. , 2016). The code to generate layered images from vector data is published as a plugin of CartAGen open source map generalisation software. Data and code for map generation are openly available here: https://doi. org/10. 5281/zenodo. 5767663.
Acknowledgments We thank  Aisling Farrell  for providing information and digital data related to C. hesternus from the collections at  La Brea Tar Pits ;  Gabriel Aguirre  for support and access to collections at the  Paleontology Institute and Museum of UZH (PIMUZ) ;  Renaud Lebrun  for the access of scanning facilities MRI platform member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  «Investments  for the future»), the  LabEx CEMEB  ( ANR-10-LABX-0004 ), and  NUMEV  ( ANR-10-LABX-0020 );  Marcelo Sánchez-Villagra  for feedback, edits, and support throughout this project. We thank the editors and anonymous reviewers for their useful comments and improvements to this work. This is ISEM publication number: n° ISEM  2023-002.
The key difficulty is that VPD and SWC covary due to land-atmosphere feedbacks  11, 12. Here, we use an Artificial Neural Network (ANN)  13  to separate the sensitivities of GPP, stomatal behavior and photosynthetic rates to SWC and VPD, based on daily data from flux tower observations with global coverage and a new European dataset that captures a recent extreme drought. Our aims are (1) to assess under what SWC and VPD conditions is GPP most negatively affected by droughts, (2) to test the hypothesis that GPP reduction induced by partial stomatal closure in response to decreasing SWC is partly compensated by increased photosynthetic rates to maintain carbon fixation, and (3) to evaluate whether Earth System Models (ESMs) capture the relative influence of VPD and SWC on GPP. The third aim is important because future projections of the land carbon sink depend on how models capture the response of GPP to atmospheric and soil droughts: increased exposure of plants to higher VPD from warming and drier continental relative humidity is inevitable and widespread  14  , whereas changes in rainfall leading to SWC deficits vary across regions  15.
The positive linear regression The negative sign for the slope between GPP and SWC means that GPP is reduced when soil becomes drier, while the negative sign for the slope between GPP and VPD means that GPP is reduced when VPD increases. slope between GPP and air temperature was slightly lower in 2014-2018 than in 2014-2017 while they were similar for incoming shortwave radiation (Supplementary Fig. 2 ). This illustrative analysis with a linear model revealed a nonlinear sensitivity of GPP to SWC, with a disproportionate negative effect of decreasing SWC under very dry soils conditions, and a more constant sensitivity of GPP to VPD. Thus, nonlinear analysis must be used. Yet, it is not clear if different sensitivities to SWC and VPD under dry and wet soils prevail globally. To test whether this response pattern is generalizable, we combined the ICOS European EC data with the global FLUXNET2015 dataset, and calculated the sensitivities of GPP to SWC and VPD using the nonlinear ANN models  13.
To illustrate this, we gave an example and showed the ANN outputs obtained at the FR-LBr site (Supplementary Fig. 3 ). Results showed that predicted GPP from ANNs tracked the observed GPP well for training, testing, and validation (Supplementary Fig. 3a, b, c ). At this site, the negative sensitivity of GPP to SWC deficits increases when soils get drier, whereas the negative sensitivity to high VPD prevails across the full range of SWC (Supplementary Fig. 3e, f ). Next, we systematically calculated the sensitivities of GPP to SWC and VPD using ANNs at each site in the ICOS European EC data and the global FLUXNET2015 dataset; the median values were used for each bin across all sites (Methods). Consistent with evidence from the mega-drought of 2018 in Europe, we found that drought significantly increased the negative sensitivity of GPP to SWC when SWC hits its lower percentiles (<70th percentiles), whereas the sensitivity to VPD was rather insensitive to the percentile of background SWC upon which a VPD increase occurs (Fig.
Methods Eddy-covariance observations. We used half-hourly or hourly GPP, air temperature, VPD, SWC and incoming shortwave radiation from the recently released ICOS (Integrated Carbon Observation System)  44  and the FLUXNET2015 dataset of energy, water, and carbon fluxes and meteorological data, both of which have undergone a standardized set of quality control and gap filling  19. Data were already processed following a consistent and uniform processing pipeline  19. This data processing pipeline mainly included: (1) thorough data quality control checks; (2) calculation of a range of friction velocity thresholds; (3) gap-filling of meteorological and flux measurements; (4) partitioning of CO 2 fluxes into respiration and photosynthesis components; and (5) calculation of a correction factor for energy fluxes  19.
All the corrections listed were already applied to the available product  19. We used incoming shortwave radiation, temperature, VPD, and SWC that were gap-filled using the marginal distribution method  21. The GPP estimates from the night-time partitioning method were used for the analysis (GPP_NT_VUT_REF). SWC was measured as volumetric SWC (percentage) at different depths, varying across sites. We mainly used the surface SWC observations but deeper SWC measurements were also used when available. Data were quality controlled so that only measured and good-quality gap filled data (QC = 0 or 1) were used. Fig. 4  The sensitivity of gross primary production to soil water content and vapor pressure deficit using five Earth System Models. a-j The sensitivity of gross primary production (GPP) to soil water content (SWC) and vapor pressure deficit (VPD) using five Earth System Models.
k-t The differences (modeled sensitivity minus observation-based sensitivity) for GPP to SWC (k-o) and VPD (p-t). Analysis of the extreme summer drought in 2018 in Europe to prove nonlinearity. To analyze the effect of summer drought in 2018 on GPP in Europe, we selected 15 sites with measurements during 2014-2018 from the ICOS dataset, representing the major ecosystems across Europe (Supplementary Table  1 ). Croplands were excluded due to the effect of management on the seasonal timing of ecosystem fluxes, both from crop rotation that change from year to year and from the variable timing of planting and harvesting. In croplands, the changes of GPP anomalies across different growing season could be mainly depend on crop varieties and management activities. Information of crop varieties, growing times yearly and other management data for each cropland site should be collected in future in order to fully consider and disentangle the impacts of SWC and VPD on its photosynthesis.
1a. Daily time series of GPP, SWC and VPD during summer for each site were normalized (z-scores) to derive the standardized sensitivity of GPP to SWC and VPD. For each variable, the mean value across the summer of 2014-2018 was subtracted for each day at each site and then normalized by its standard deviation. At each site, we used a multiple linear regression (Eq. 2) to estimate daily GPP anomalies sensitivities to SWC and VPD anomalies across 2014-2018 and 2014-2017, respectively: GPP ¼ β 1 SWC þ β 2 VPD þ β 3 SWC VPD þ β 4 T a þ β 5 RAD þ b þ ε ð2Þ where β i is the standardized sensitivity of GPP to each variable; T a represents the air temperature; RAD represents the incoming shortwave radiation; b represents the intercept; and ε is the random error term. We compared estimated sensitivities with and without 2018 data to quantify the impacts of extreme drought in 2018 on GPP sensitivity to SWC (Fig.
We retrieved the daily A max by implementing Eqs. (  6 ) and (  7 ) using the REddyProc R package (https://github. com/bgctw/REddyProc)  20. V cmax represents the activity of the primary carboxylating enzyme ribulose 1,5bisphosphate carboxylase-oxygenase (Rubisco) as measured under light-saturated conditions. To evaluate the responses of V cmax to SWC and VPD, we first calculated the daily internal leaf CO 2 partial pressure (c i ) in the middle of the day (11:00-14:00) via Fick's Law (Eq. 8), excluding periods with low incoming shortwave radiation (<500 W m -2 ). c i ¼ c a À GPP ðr co2 þ r a Þ ð 8Þ where c a is the atmospheric CO 2 partial pressure, and r co2 is the ecosystem resistance to CO 2 (1. 6/G c ). Then we derived V cmax according to the standard biochemical model (Eq. 9): A max ¼ V cmax ðC i À Γ Ã Þ ðC i þ KÞ ð9Þ where Γ* is the CO 2 compensation point in the absence of mitochondrial respiration and K is the effective Michaelis-Menten coefficient of Rubisco.
Both Γ* and K are temperature-dependent variables  50. Values of V cmax were standardized to 25 °C using the Arrhenius equation with activation energies from Bernacchi et al. 51, 52. Artificial neural networks and sensitivity analysis. ANN has been used with eddy covariance datasets  [53] [54] [55]  and remote sensing datasets  13, 42, 56  in the Earth sciences as predictive or analysis tool. We used the ANN to analyze the sensitivities of GPP, A max , V cmax , G c and iWUE to SWC and VPD. ANN was chosen for this application because it has nonlinear activation functions, which can effectively predict nonlinear effects  13, 54, 57. We limited the ANN fit to the small number of predictors that are known environmental drivers, in order to avoid over-fitting  54. Daily temperature, VPD, SWC and incoming shortwave radiation were used as predictor variables while daily GPP (or G c , A max , V cmax , iWUE) is used as a response variable.
Feed-forward ANN (one hidden layer) was trained using the Matlab 'Neural fitting toolbox' and repeated five times. The number of nodes in the hidden layer was sampled from 4 to 20 (step size 2), and 10 was selected because the results from different nodes were very similar. 60% of the data were used for the purpose of training the ANN while the remaining 40% of the data were used for validation (20%) and testing (20%). Performance was assessed by correlations (r) and root-mean-square errors. Results showed the r values were >0. 7 at most sites. During the training process, weight and bias values were optimized using the Levenberg-Marquardt optimization  58, 59. The maximum number of epochs to train is 1000. An example to demonstrate the ANN training at one site was shown in Supplementary Fig.
3. At each site, ANN was run and sensitivities were calculated for all data within each SWC and VPD bin and the median value was used. For each of the five trained ANNs, one of the predictor variables was perturbed by one standard deviation (a value of 1 due to the initial input data normalization), and GPP was predicted again using the existing ANN with the predictors including the perturbed variable; this process was repeated for each predictor variable. The predicted values of GPP obtained with and without perturbation were then compared to determine the sensitivity values. The sample equation showing the calculation of the GPP sensitivity to VPD is shown in Eq. (10). Sensitivity VPD ¼ median GPP ANN VPDþstdev VPD ð Þ ð Þ À GPP ANN all VAR ð Þ stdev VPD ð Þ ð10Þ We repeated the ANN and sensitivity analyses five times and the median of these were used at each site.
To test if the phenological cycle affects our results, we repeated our analysis (1) using only peak growing season, the 3-month period with the maximum mean GPP across the available years, where seasonal variability is muted; (2) using anomalies by removing the seasonal cycle, which was calculated by averaging all available years of the data and smoothing the series with a 30-day moving average as Feldman, Short Gianotti 60. Both analyses yield similar results (Supplementary Fig. 9 ). The main sources of uncertainty for G c is the latent heat flux uncertainty from eddy covariance measurements. We used both the 'LE' and 'LE. CORR' variables reported by the ICOS and FLUXNET2015 database for latent energy exchange. LE. CORR is the "energy balance corrected" version of latent heat flux, based on the assumption that Bowen ratio is correct.
Following the observational analysis, the same analysis was carried out for the five CMIP6 models. Each variable was first normalized using z-scores for each site over the growing season, and an ANN was created at each site for each model. Similar to the observational analysis, ANN and sensitivity analyses were performed five times and the median of these were used. At each site, sensitivities were calculated for all data within each SWC and VPD bin and each bin was summarized by its median value. The median values of sensitivities across all sites for each bin were used for the results. To evaluate the sensitivity performance in ESMs, we calculated the difference between modeled and observed sensitivities (Fig. 4 ).
Acknowledgements This work was financially supported by the  European Research Council  Synergy project SyG-2013-610028 IMBALANCE-P (to Z. F. and P. C. ) and the  ANR CLAND Convergence Institute  (to Z. F. , P. C. and D. M. ). I. C. P acknowledges support from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No:  787203  REALM). This work is a contribution to the  Imperial College initiative on Grand Challenges in Ecosystems and the Environment  (to I. C. P). P. C. S acknowledges support from the  U. S. National Science Foundation  award  DEB-1552976. P. G. acknowledges funding from  LEMONTREE Schmid  futures grant and  NASA  grants  NNH17ZDA00IN-THP  and  NNX16AO16H. We would like to thank the  ICOS Infrastructure  for support in collecting and curating the eddy covariance data for 2018.