dataesr/scientific-paragraphs-categorization huggingface dataset:
The LPJmL model requires non-climatic datasets as well. For details, please refer to the setup section of the following references  (Biemans et al. , 2019; Wijngaard et al. , 2018). Land use information for two cropping seasons (rabi and kharif) including 13 crops, both rainfed and irrigated, with adjusted crop fractional areas for South Asia (corrected to match recent agricultural statistics of the region) was derived from the MIRCA2000 dataset  (Biemans et al. , 2016; Portmann et al. , 2010). Soil information based on the Harmonized World Soil Dataset (HWSD) was aggregated to a 5-minute resolution. Zonespeci c, monsoon-dependent sowing dates were used as in  (Biemans et al. , 2016). To get drainage directions, void-lled hydrological DEM and stream network data were used from the HydroSHEDS dataset at 5 arcmin spatial resolution  (Lehner et al.
Model Runs To estimate the crop-speci c irrigation water demand and supply by sources, the LPJmL model is rst run to establish an equilibrium between the carbon pools (soil and vegetation) and water uxes (soil and surface water). For this, the model is run for two spin-up periods i. e. 1000 years with natural vegetation and 300 years with the land-use le, using daily climate input of WATCH forcing data ERA-Interim (WFDEI). Subsequently, the model is run for a control period (1981-2010) and a scenario period (2011-2100). To simulate crop-speci c (wheat and rice) irrigation water demand and supply data, 1 st November is used as a single sowing date for wheat in the rabi season throughout the study domain. For rice in the kharif season, zone-speci c monsoon dependent sowing dates are used, which helped to capture existing hydro-climatic heterogeneity in the region  (Biemans et al.
Only that part of the crop water demand is ful lled as available from surface water The difference between the two model run is used to estimate the irrigation water supply from groundwater. To extract the crop growth phase-speci c (vegetative, reproductive and ripening) irrigation water demand and supply data, we used the crop phenological development variable simulated by LPJmL following the approach developed by  (Ahmad et al. , 2021). Based on this methodology, start and end dates of each phenology phases were used to extract crop-speci c irrigation water demand and supply data for the different phenology phases for each year and each grid cell using a script developed in R version 3. 6. 1 (Xie et al. , 2018).
Uncertainties in the model setup can be associated with the use of the land-use data, a xed irrigation system and a single sowing date. The uncertainty in our results could be associated with the land use data where we made some adjustments in the observed MIRCA2000 dataset. MIRCA is a global data set contain information on irrigated and rainfed monthly crop areas for 26 crop classes around the year 2000. For our study, the adjustments were made rst to aggregate the crop classes as available in the LPJmL model. Next the seasonal cropped areas from the monthly MIRCA cropping calendars were decided i. e. , the kharif season area is de ned as the area under cultivation per crop as in September and the area per crop in January is taken as rabi cropped area. The adjustments were also made in crops rotation and crop areas.
Study design and ethical considerations The current study is a subgroup analysis of a prospective monocentric trial (APOCOGNIT, ClinicalTrial. gov NCT02574234). Patients were enrolled in the orthopedic service of the University Hospital of Toulouse (Toulouse, France), where an average of 800 hip fracture patients attend every year. The inclusion was performed from March 2016 to March 2018. APOCOGNIT was approved by the Clinical Research Ethics Committee of the University Hospital of Toulouse in January 2016, with a decision number of 14 7313 02. Written informed consent was obtained from all patients who agreed to participate in the APOCOGNIT study.
Data collection Preoperative assessment Patients underwent a standardized interview conducted by an anesthesiologist. Demographic characteristics, behavioral factors, physical function, and coexisting conditions were assessed. In addition, by reviewing the patient's anesthesia records, the American Society of Anesthesiologists Classification was obtained. The Charlson comorbidity index, a weighted sum of 17 medical conditions, was also calculated  (19). Preoperative cognitive functions were measured using the MMSE. This scale was chosen because it is easy to use, as previously described  (20). Functional capacities were assessed using the instrumental activities of daily living (IADL), which ranged from 0 (complete dependence) to 8 (complete independence).
Statistical analysis APOCOGNIT was an exploratory study designed to investigate the postoperative inflammatory profile of patients in relation to the occurrence of postoperative delirium. For the APOCOGNIT project, the number of subjects included was 105, with the aim of including at least 40 patients with postoperative delirium. We investigated the data of the subgroup of patients with 3month follow-up. For descriptive statistics, the results were expressed as median and 95% CI. The study population was then divided into two groups based on the occurrence of institutionalization at 3 months after hip fracture. After verification of the absence of normality of quantitative data (Anderson-Darling test), patient characteristics were compared using non-parametric tests (Mann-Whitney U-test for continuous variables and chi-square exact test for categorical variables).
The association between different covariates and dependent variables (institutionalization) was calculated using a multivariate analysis, setting an initial threshold at a p-value of < 05. After the exclusion of collinear covariates, stepwise regression (backward elimination) was applied, starting with all the variables initially chosen and then progressively removing non-significant ones. We used XLSTAT R version 2019 1. 1 statistical software (Addinsoft 2020). A p-value of < 0. 05 was considered to be statistically significant.
, 2015). Recent studies have suggested that data assimilation (DA) of remotely sensed products is the path forward to estimate the spatial distribution of relevant snowpack characteristics  (Aalstad et al. , 2018; Charrois et al. , 2016; Cortés and Margulis, 2017; Margulis et al. , 2016; Smyth et al. , 2020; Stigter et al. , 2017). Using DA techniques it is possible to fuse model simulations and multiple remote sensing datasets to improve the snowpack simulations. In particular, the snow cover area has been assimilated in many case studies due to its widespread availability (e. g. Baba et al. , 2018; Alonso-González et al. , 2021 ). Yet, the extent of the snow cover provides no direct information on the internal state of the snowpack and is blind to snowpack changes when the pixel is fully snow covered  (De Lannoy et al.
Synthetic experiments were also used to explore the potential of data assimilation techniques in improving the snowpack simulations  (Clark et al. , 2006; Revuelto et al. , 2021; Smyth et al. , 2019). Here, we designed an OSSE to evaluate the benefit of future remote sensing LST to simulate seasonal SWE. In this experiment, synthetic LST and SWE data were generated in several climatic regions. Synthetic LST data were assimilated into a snowpack model under different cloud cover scenarios and with different satellite revisit times. The benefit of assimilating LST was studied by comparing the posterior (after assimilation) SWE to the synthetic SWE. 2 Data and methods.
Data assimilation experiments The degraded meteorological forcing and synthetic LST were used to feed the Multiple Snow Data Assimilation System (MuSA). MuSA is an open-source ensemble-based data assimilation toolbox built around FSM2 (Alonso-  González et al. , 2022). We used the same initial conditions to run FSM2 within MuSA (soil temperature profile, initial LST and absence of snow); therefore we did not perform a spin-up. To emulate the differences between the reality and modelling pipeline, we used a simplified parameterization scheme of FSM2 in MuSA (Table  2 ). The assimilation experiments were done using a particle batch smoother (PBS)  (Margulis et al. , 2015)  and a particle filter (PF). Smoother algorithms are typically used to develop reanalyses, as all time series of information are available, whereas filtering is used for operational forecasting where future observations with respect to the analysis step are not available  (Largeron et al.
This corresponds to 720 000 FSM2 runs (300 particles by MuSA run), summing up to 3 600 000 FSM2 runs considering the five study areas, which is equivalent to simulating the snowpack over a period of 14 400 000 years. To compare the performance of data assimilation, in ev-ery site regardless of the absolute magnitude of the SWE, we used the normalized RMSE (nRMSE) as a performance score: nRMSE = 1 n n i=1 (Pred i -Ref i ) 2 Ref , where n is the number of samples.
Conclusions The motivation for the study of LST data assimilation is the upcoming launch of high-resolution thermal infrared spatial missions with an improved revisit time and resolution in the next few years. We implemented a synthetic data assimilation experiment to study the potential of LST in improving SWE simulations along a latitudinal gradient in the Northern Hemisphere. The methodology was based on the generation of synthetic LST estimations and true SWE estimates, as well as a prior ensemble of SWE simulations generated by forcing FSM2 with degraded meteorological fields. The MuSA snow data assimilation software was used to generate SWE posterior time series using the particle batch smoother and particle filter algorithms to be compared with synthetic true SWE. The results suggest that the assimilation of LST has great potential to improve seasonal snowpack simulations across all the tested sites.
Code and data availability. The MuSA v1. 0 code is available at https://github. com/ealonsogzl/MuSA/tree/v1. 0 (last access: 8 August 2023; DOI: https://doi. org/10. 5281/zenodo. 7014570, Alonso-González, 2022). The original FSM2 code is found at https://github. com/RichardEssery/FSM2 (Essery, 2015)  and in the MuSA repository with slight modifications to the original version. ERA5-Land data are available for download from the Copernicus Climate Data Store (https://cds. climate. copernicus. eu/, Copernicus Climate Data Store, 2023). https://doi. org/10. 5194/tc-17-3329-2023 The Cryosphere, 17, 3329-3342, 2023.
Here we will build on our implementation of the Integrative Membrane Protein and Lipid Association Method (IMPALA) for interactive simulations  [15] , which is based on the Fauchère-Pliska scale. Our goal is to extend the previous methodology to achieve energetically accurate prediction of protein insertion into implicit membranes of arbitrary shape. This goal is directly related to the development of a software package optimized for specific user functions such as real-time responses, live visual analysis, and virtual reality experiences. The IMPALA reference technique originated from Brasseur's research team to define the relationship between different proteins and a membrane structure. We chose this approach because it is simple and computationally efficient enough to lend itself to live calculations with user interaction and possible inclusion in an integrative modeling process.
It is the values of E tr(i) that are then iteratively adjusted until the resulting E IMP matches E tr/EXP. There are many ways to adjust the initial parameters to reproduce target values. Here we have used four simple approaches that we developed for implementing UNILIPID. Our overall goal was to minimise deviations from the initial atom type parameterization as long as the target energy is calculated correctly. Therefore, the parameterizations that provide the correct energies are not unique. For our purposes, we selected the parameterization from the four approaches for each amino acid that required the fewest changes compared to the initial set. The detailed description of these methods for calibrating atomic types have been moved to the Supplementary Materials S2. 1. 1 to S2. 1. 4, so the core of the manuscript focuses more on the methodology and less on the specific implementation of the calibration approaches.
8 Å. Other parameter sets for higher or lower temperatures and for additional phospholipids such as PS, PI, SM, and cholesterol could be derived if coherent structural reference parameters are obtained. These parameters only mimic the structural properties of these bilayers, but have no effect on the insertion energetics expressed by the parameters E tr(i) and a lip. Such an energetic reparameterization would require knowledge of a consistent set of lipid-specific transfer energy measurements, to which we currently do not have access. It should be noted, however, that such reparameterization is entirely possible. Since we do not currently have such parameters, we have not implemented such a feature. Should such data become available in the future, UNILIPID could easily be adapted to mimic membranes composed of multiple lipid species.
Required mesh data includes the list of vertices, triangles, and normals. This data is sent from UnityMol, which acts as a graphical user interface, to the BioSpring simulation engine to calculate the corresponding UNILIPID potential. This data is transparently exchanged via the MDDriver middleware. Note that in developing such a software implementation, we focus in particular on interactive simulations. Such simulations are very special in many respects, and because of their particular requirements, otherwise commonly used visualization software cannot easily be used for interactive simulations. For simplicity, we provide a general video tutorial in the data associated to the manuscript to more clearly demonstrate the use case of this interactive membrane insertion and to guide the user through the use of the software.
Rather than explicitly defining the membrane surface through an equation (Figure  5 ), the user can provide a custom mesh that can be designed with third-party software or possibly derived from experimental data such as image stacks by segmentation  [36, 37]. Required mesh data includes the list of vertices, triangles, and normals. This data is sent from UnityMol, which acts as a graphical user interface, to the BioSpring simulation engine to calculate the corresponding UNILIPID potential. This data is transparently exchanged via the MDDriver middleware. to show or hide these la er layers, as it can sometimes be interesting not to show them in order to observe certain aspects of the insertion in more detail (Figure  4 ). Another possibility is to vary the curvature of the membrane, where curvature is defined as the reciprocal of the radius of a sphere.
In the procedure where we loop over all vertices, we can apply any mathematical formula to the z (vertical) component of the vertex that is a function of the x (width) and y (length) components. For the curvature, for example, we obtain in this way the equation for a sphere: = ± ---. Rather than explicitly defining the membrane surface through an equation (Figure  5 ), the user can provide a custom mesh that can be designed with third-party software or possibly derived from experimental data such as image stacks by segmentation  [36, 37]. Required mesh data includes the list of vertices, triangles, and normals. This data is sent from UnityMol, which acts as a graphical user interface, to the BioSpring simulation engine to calculate the corresponding UNILIPID potential. This data is transparently exchanged via the MDDriver middleware.
Flexible Elastic Network Representation of the Protein Some interactive experiments require flexibility of the protein. An efficient way to achieve this effect is via elastic network models. We have previously described the methodoloy in  [35, 38, 39]. We have implemented such features in the BioSpring software, which uses additive energy and force terms to represent the various contributions to a given model representation. BioSpring features an extended elastic network model (aENM) that combines the spring network with non-bonded terms between atoms or pseudoatoms. The aENM model supports the use of multiple cut-off distances to determine a multicomponent spring network. In this study, a single cut-off distance of 9Å without nonbonded terms was used.
Flexible Elastic Network Representation of the Protein Some interactive experiments require flexibility of the protein. An efficient way to achieve this effect is via elastic network models. We have previously described the methodoloy in  [35, 38, 39]. We have implemented such features in the BioSpring software, which uses additive energy and force terms to represent the various contributions to a given model representation. BioSpring features an extended elastic network model (aENM) that combines the spring network with non-bonded terms between atoms or pseudoatoms. The aENM model supports the use of multiple cut-off distances to determine a multi-component spring network. In this study, a single cut-off distance of 9 Å without non-bonded terms was used.
Two types of input are needed for this purpose: a database of PDB structure files to derive the surface information per particle, and a correspondence file linking particle types to their reference transfer energies. The following Figure  7  shows the comparison of the transfer energy for each side chain for the experimental scale with the original IMPALA implementation and with the UNILIPID reparameterization. It is adapted from the data in the original Figure  3  in the article by Ducarme  [26]. From this figure, it can be seen that all side chains now reflect the reference energy scale with high accuracy. In particular, amino acids such as lysine, for which the wrong sign of the transfer energy was originally found, now reproduce the expected physicochemical properties. Figure  8  shows in detail the changes made for each atom type during parameter calibration.
We use the scales for transfer to hexadecane and to octanol to calibrate two Martini 3 parameter sets for our experiments in UNILIPID. In order to adapt the Martini 3 parameters, it was necessary to adapt the algorithm for managing the residue representation-and thus the particles. We used the database of conformations mentioned above, from which all amino acids are then extracted. The tool martinize  [41]  is used to generate structures and topologies. The structures are used to calculate for each amino acid the surface area of the grains they form in the Martini representation, using the tool FreeSASA  [42]. The topologies are used to define for each amino acid the types of Martini 3 grains and associate them with their transfer energy, which is contained in "APPENDIX 3" of the summary document "Summary_v3.
It specifically compares data such as time/angle/depth/frame for the original ("ideal") recalibrated IMPALA implementation in UNILIPID that still uses an analytical membrane with the mesh-based approach where the mesh grid spacing is decreased from 10 to 1 Å to progressively increase resolution. The results are from a single simulation session of 6 min of interactive insertion, without human intervention, with stabilization of about 30s for each grid spacing parameter set. The experiment was performed with both UnityMol and BioSpring running on the same computer. All calculations were performed on multiple threads using OpenMP parallelization of our BioSpring software on an Intel ® Core™ i5-10500 @ 3. 1 GHz (released in 2020)-6 cores, using 8 threads. The nearest vertex search algorithm was not optimized.
The OmpA all-atom representation contains 2586 particles for the whole system. The number of grid points that needs to be probed for each resolution in the order [10→1] are  [100, 100, 144, 169, 225, 324, 484, 784, 1764, 6724]. running on the same computer. All calculations were performed on multiple threads using OpenMP parallelization of our BioSpring software on an Intel ® Core™ i5-10500 @ 3. 1 GHz (released in 2020)-6 cores, using 8 threads. The nearest vertex search algorithm was not optimized. The OmpA all-atom representation contains 2586 particles for the whole system. The number of grid points that needs to be probed for each resolution in the order  Comparison of the analytical membrane representation with a mesh representation for the OmpA membrane protein shows that the insertion depth stabilizes at about -6 Å.
Changing Representation Scales We have performed Monte Carlo insertion experiments for two other representation scales: the reparametrized Martini 3 representation described in Section 3. 2 and a onebead-per-amino acid representation of Levitt  [29]. The latter scale is "trivial" in the sense that, since there is only one bead for each amino acid, the transfer energy of each bead corresponds a priori exactly to the experimental reference value for that amino acid. For this experiment, we used maltoporin (PDB code 1AF6) from  [43]. Figure  10  shows the results, highlighting the distribution of transfer energies for the three levels of representation (A-C) and the forces acting on the inserted system in the Martini representation (D). It provides a pictorial representation of the distribution of transfer energy with a color code for each bead.
Changing Representation Scales We have performed Monte Carlo insertion experiments for two other representation scales: the reparametrized Martini 3 representation described in Section 3. 2 and a onebead-per-amino acid representation of Levi  [29]. The la er scale is "trivial" in the sense that, since there is only one bead for each amino acid, the transfer energy of each bead corresponds a priori exactly to the experimental reference value for that amino acid. For this experiment, we used maltoporin (PDB code 1AF6) from  [43]. Figure  10  shows the results, highlighting the distribution of transfer energies for the three levels of representation (A-C) and the forces acting on the inserted system in the Martini representation (D). It provides a pictorial representation of the distribution of transfer energy with a color code for each bead.
Application to a Double Membrane System: Membrane Fusion via the SNARE Complex Here we look at a protein complex involved in exocytosis, in which molecules stored in lipid vesicles are transported from inside a cell to its surroundings. The final step of this process requires fusion of the vesicles to the plasma membrane and is mediated by SNARE (soluble N-ethylmaleimide sensitive factor attachment protein receptor) fusion proteins. We consider a model consisting of a four-helical SNARE bundle  [44] , which we have previously studied in interactive molecular dynamics simulations  [45]. This complex comprises two transmembrane domains (TMDs) that anchor into the two membranes to be fused. We are pursuing the insertion of such a model, represented at the level of an elastic network model, using our BioSpring software, as described in  [35].
Application to a Double Membrane System: Membrane Fusion via the SNARE Complex Here we look at a protein complex involved in exocytosis, in which molecules stored in lipid vesicles are transported from inside a cell to its surroundings. The final step of this process requires fusion of the vesicles to the plasma membrane and is mediated by SNARE (soluble N-ethylmaleimide sensitive factor a achment protein receptor) fusion proteins. We consider a model consisting of a four-helical SNARE bundle  [44] , which we have previously studied in interactive molecular dynamics simulations  [45]. This complex comprises two transmembrane domains (TMDs) that anchor into the two membranes to be fused. We are pursuing the insertion of such a model, represented at the level of an elastic network model, using our BioSpring software, as described in  [35].