dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysing simulations In practise, simulations in the NMRlipids Databank can be analyzed by executing a program that 1) loops over the README. yaml files in the Databank layer, 2) downloads the data using the information in the README. yaml files, and then 3) performs the desired analysis on a local computer utilising the universal naming conventions for molecules and atoms defined in the README. yaml and mapping files. This general procedure is illustrated in Fig. 1D , and a templates for user-defined analyses are available via NMRlipids Databank documentation  19. Further practical examples of codes performing such analyses are listed in Tables  S1  and S2. The equilibration period given by the user (TIMELEFTOUT in Table  S3 ) is discarded from the trajectories in all analysis codes. For further details, see the NMRlipids databank documentation nmrlipids.
Rigid molecules that do not exhibit significant conformational fluctuations, such as sterols, were excluded from the analysis. In practise, the distribution convergence times were calculated utilising its linear dependence on autocorrelation decay times, τ convergence = kτ autocorrelation , because calculation of autocorrelation decay times is faster and computationally more stable than direct calculation of distribution convergence times  70, 71. The empirical coefficient k = 49 was calculated based on the analysis of 8 trajectories with the length of more than 200 ns, including simulations of POPC, POPS, POPE, POPG, and DPPC with the CHARMM36 force field. Because the coefficient k does not depend on the force field  70  , the value determined from these CHARMM36 simulations can be used for all simulations in the Databank.
Calculation of C-H bond order parameters The C-H bond order parameters were calculated directly from the carbon and hydrogen positions using the definition S CH = 1 2 3 cos 2 θ -1 , (1) where angular brackets denote the ensemble average, i. e. , average over all sampled configurations of all lipids in a simulation, and θ is the angle between the C-H bond and the membrane normal. As in previous NMRlipids publications, the order parameters were first calculated separately for each lipid and the standard error of the mean over different lipids was used as the error estimate  6. However, order parameters for simulations with τ rel > 1 may be influenced by the starting structure and thereby their error bars may be underestimated. The script that calculates C-H bond order parameters from all simulations in the NMRlipids Databank is available at /Scripts/AnalyzeDatabank/calcOrderParameters.
Calculation of X-ray scattering form factors X-ray scattering form factors were calculated using the standard equation for lipid bilayers that does not assume symmetric membranes  13  , F(q) = D/2 -D/2 ∆ρ e (z) exp(izq z )dz , (2) where ∆ρ e (z) is the difference between the total and solvent electron densities, and D is the simulation box size in the z-direction (normal to the membrane). For the calculation of density profiles, atom coordinates were first centred around the centre of mass of lipid molecules for every time frame, and a histogram of these centred positions, weighted with the number of electrons in each atom, was then calculated with the bin width of 1/3 Å. Electron density profiles were then calculated as an average of these histograms over the time frames in simulations. The script to calculate form factors for all simulations in the NMRlipids Databank is available at Scripts/AnalyzeDatabank/calc_FormFactors.
1 Å -1 from both simulation (FF sim min ) and experiment (FF exp min ). The quality of a form factor is then defined as the Euclidean distance between the minima locations: FF q = |FF sim min -FF exp min | × 100. The quality evaluation of form factors is implemented in /Scripts/BuildDatabank/QualityEvaluation. py in the NMRlipids Databank repository. The resulting form factor qualities are stored in files named FormFactorQuality. json at folders in /Data/Simulations in the NMRlipids Databank repository.
Training machine learning model to predict properties of multi-component membranes Different machine learning approaches were tested to find the best model to predict the area per lipids of multi-component membranes from their lipid composition. To his end, we tested linear, Lasso, Ridge, elastic net, decision tree, random forest, multi-layer perceptron, k-nearest neighbour, gradient boosting, AdaBoost, and XGBoost regressors. The models were trained to predict the area per lipid for a membrane with the given molar fractions of membrane lipids. Most abundant lipids in the databank, POPC, POPE, POPG, POPS and cholesterol, were considered explicitly, while SM, cardiolipins and PI lipids with more sparse data were all grouped together based on their headgroups independently on acyl chain content.
Salt concentration, temperature and other conditions were not considered. To find the optimal model for this task, we first randomly divided simulations into training (80% of the simulations) and test (20% of the simulations) sets. Then, we used the training set with 5-fold cross validation to conduct a grid search for hyperparameters for the set of ML regression models. The best performing hyperparameters were then used to fit each model to the training set. The performance of the models was then tested on the remaining 20% of the data extracted from the databank, and additionally, by predicting area per lipids from multi-component membrane simulations reported in the literature but not included in the databank  [25] [26] [27]. Due to its simplicity and good performance in these tests, we selected to use linear regression model for further studies.
The linear regression model predicting membrane thicknesses was trained similarly. The ML models and analysis were implemented with Python using scikit-learn which was supplemented with the xgboost library to include extreme gradient boosted decision tree regressor to the set of tested model. Jupyter notebook that trains the models and predicts are per lipids is available at scripts/APLpredictor. ipynb in the repository at https://github. com/NMRLipids/DataBankManuscript/. In further applications, it is important to consider potential limitations arising from the currently limited amount of data for certain types of systems. For example, the current models have been trained and tested only for mixtures with palmitoyl and oleyl acyl chains because the databank does not yet contain enough data with varying number of double bonds.
Calculation of lipid flip-flops Flip-flop rates were calculated using the AssignLeaflets and FlipFlop tools from the LiPyphilic package  75. Headgroup atoms of each molecule, as defined in the mapping file, were used to determine in which leaflet the molecule locates. The midplane cut-off, defining the region between leaflets, was 1 nm and the frame cutoff was 100. This means that if the headgroup of a molecule entered within the distance of 1 nm from the bilayer midplane and was found in the opposing leaflet after 100 steps, this event was considered as a successful flip-flop event. The code that finds the flip-flop events from all simulations in the NMRlipids Databank is available at scripts/FlipFlop. py, and the results at Data/Flipflops/ in the repository at https://github. com/NMRLipids/DataBankManuscript/.
Analysing anisotropic diffusion of water in a membrane environment from NMRlipids Databank Water permeability through membranes was calculated from equation P = r/2c w , where r is the rate of permeation events per time and area, and c w = 33. 3679 nm -3 is the concentration of water in bulk  43. The number of permeation events in each trajectory was calculated using the code by Camilo et al. 44  , available at https://github. com/crobertocamilo/ MD-permeation. The code that calculates permeabilities for all simulations in the NMRlipids Databank is available at /scripts/calcMD-PERMEATION. py, and the resulting permeabilities are stored at /Data/MD-PERMEATION in the repository containing all analyses specific for this publication at https://github. com/NMRLipids/DataBankManuscript/. This repository is organized similarly to the NMRlipids Databank repository, enabling the upcycling of also the analyzed data without overloading the main NMRlipids Databank repository.
The lateral diffusion of water along the membrane surface, D ∥ , was calculated with the Einstein's equation using the -lateral option in the gmx msd program within the Gromacs software package  76. The code that calculates D ∥ for water from all simulations in the NMRlipids Databank is available at /scripts/calcWATERdiffusion. py, and the resulting diffusion coefficients are stored at /Data/WATERdiffusion in the repository at https://github. com/NMRLipids/ DataBankManuscript/. Water diffusion along the perpendicular direction of lipid bilayers in a multilamellar stack was estimated from the Tanner equation D ⊥ = D ∥ Pz w D ∥ +Pz w 51, 52 , where the water layer thickness, z w , was estimated by subtracting the bilayer thickness from the size of the simulation box in the membrane normal direction.
Acknowledgements P. B. was supported by the  Academy of Finland  (Grants  311031  and  342908 ). R. G. -F, A. P. and F. S. -L. thank  Centro de Supercomputación de Galicia  for computational support. R. G. -F. thanks  Ministerio de Ciencia, Innovación y Universidades  for a " Ramón y Cajal " contract ( RYC-2016-20335 ), and  Spanish Agencia Estatal de Investigación (AEI)  and the  ERDF  ( RTI2018-098795-A-I00 ,  PDC2022-133402-I00 ),  Xunta de Galicia  and the  ERDF  ( ED431F 2020/05 ,  02_IN606D_2022_2667887  and  Centro singular de investigación de Galicia  accreditation  2016-2019 ,  ED431G/09 ). A. P. thanks  Spanish Agencia Estatal de Investigación (AEI)  and the  ERDF  ( PID2019-111327GB-I00 ,  PDC2022-133402-I00 ),  Xunta de Galicia  and the  ERDF  ( ED431B 2022/36 ,  02_IN606D_2022_2667887 ).
F. S. -L. thanks  Axencia Galega de Innovación  for his predoctoral contract ( 02_IN606D_2022_2667887 ), and  Spanish Agencia Estatal de Investigación (AEI)  and the  ERDF  ( PID2019-111327GB-I00 ). F. F. -R. acknowledges  Tecnológico Nacional de México ,  Dirección General de Asuntos del Personal Académico (DGAPA) ,  Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (PAPIIT)   101923 ,  CONACyT Ciencia de Frontera   74884 , for financial support and  Miztli-Dirección de Cómputo y de Tecnologías de Información y Comunicación (DGTIC) -Universidad Nacional Autónoma de México (UNAM)  (Project  LANCAD-UNAM-DGTIC-057 ) facilities for computing time allocation. M. S. M. was supported by the  Trond Mohn Foundation   BFS2017TMT01. O. H. S. O, A. M. K, R. N. and L. S. B. acknowledge  CSC -IT Center for Science for computational resources and Academy of Finland  (grant nos.
315596 ,  319902  &  345631 ) for financial support. M. K. acknowledge  CSC --IT Center for Science  for computational resources and thanks  Finnish Cultural Foundation  and the  UEF Doctoral Programme  for financial support. T. M. F. greatly acknowledges financial support by the  Ministry of Economics, Science and Digitalisation of the State of Saxony-Anhalt. F. L. was supported by the  Deutsche Forschungsgemeinschaft  ( DFG LO 2821/1-1 ). We acknowledge all the NMRlipids Project contributors for making development of the NMRlipids Databank possible. L. M. acknowledges funding by the  Institut National de la Santé et de la Recherche Médicale (INSERM).
D-F Direct comparison against experimental (NMR order parameters and X-ray scattering ) data exemplified for a simulation with the best overall order parameter quality (D), the best quality for POPE lipid (E), and the headgroup quality for POPE (F). G Scatter plots and Pearson correlation coefficients, r, for the membrane area per lipid, thickness, first minimum of X-ray scattering form factor and average order parameter of the sn-1 acyl chain extracted from the NMRlipids Databank. All correlation coefficients have p-value below 0. 001. For more correlations see Fig. S2.
Method #2: Evaluation of geometric volume using the graphical method This second method is used as a validation of the previous one. It is based on the analysis of the positions of the particles during the time evolution through an examination of saved snapshots, exploiting graphical code built on MATLAB©. The particles are represented as coloured circles and their size has to be tuned in order to have a complete overlapping between the particles avoiding empty spaces. Each snapshot is then elaborated, converting the pixel of fluid in black and associating a value equal to 1 while the void regions are set in white associating the value of 0 (see Fig. 3 ). The second approximation for the error 𝜖 𝑉 is given as: 𝜖 (𝐺𝑀) 𝑉 = 𝑁 𝑏𝑙𝑎𝑐𝑘 𝑝𝑖𝑥𝑒𝑙 𝑁 𝑡𝑜𝑡 𝑝𝑖𝑥𝑒𝑙 -1 , ( 15 ) where 𝑁 𝑡𝑜𝑡 𝑝𝑖𝑥𝑒𝑙 are the total number of the pixels of the image processed while 𝑁 𝑏𝑙𝑎𝑐𝑘 𝑝𝑖𝑥𝑒𝑙 are the number of pixels representing the fluid region which are coloured in black.
The volume error measured by the graphical method, 𝜖 𝐺𝑀 𝑉 , is also plotted in Fig. 31. Because of the complexity of the flow, the 𝜖 𝐺𝑀 𝑉 trend is characterized by intense high-frequency variations. Through a filter operation on 𝜖 𝐺𝑀 𝑉 , it is possible to reach a fair agreement with 𝜖 (1)  𝑉. Fig. 32  shows the fields of the local volume errors of the 𝛿 + -ALE-SPH simulation for four different time instants during the last oscillation periods. During this time interval the water impacts only on the right vertical side. These impacts are quite violent generating a large fragmentation of the free-surface, with the formation of jets and drops. Because of these phenomena, the local volume errors are higher than the ones depicted for the INM1 test-case. Comparing the 𝛿 + -ALE-SPH results with the experimental data no-negligible differences in the free-surface configurations are found, indeed, the 3D effects are quite relevant.
Acknowledgements The research was developed within the  Project Area Applied Mathematics of the Department of Engineering, ICT and Technology for Energy and Transport (DIITET)  of the  Italian National Research Council (CNR). The work was supported by the  SLOWD  project, which received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 815044 , the  National Natural Science Foundation of China  (Grant Nos. 52171329  &  12002404 ), the  Fundamental Research Funds for the Central Universities ,  Sun Yat-sen University, China  (Grant No. 231gbi023 ), and the  GHfund A  (Grant No. 202302014084 ). This work was performed by using the HPC resources of the  Centrale Nantes Supercomputing Centre  on the cluster Liger and the  OceanConnect High-Performance Computing Cluster of Sun Yat-sen University.
Genotyping For DNA extraction, the QIAampR circulating nucleic acid kit (Qiagen, Hilden, Germany) was used following the manufacturer's instructions. One plasma sample was extracted using the COBAS extraction kit (Roche, Rotkreuz, Switzerland) without cfDNA dosage, which explains the missing data for paired sample number 15 in Table  1. Extracted DNA was analyzed using either highthroughput sequencing techniques or COBAS technology, depending on the period of time of the molecular analysis in our laboratory. Two different highthroughput sequencing techniques were used. The first technique was based on the Tumor Hotspot MASTR Plus kit (Multiplicom, Agilent Technologies, Santa Clara, CA) for library creation and the MiSeq sequencer (Illumina). Data were aligned using BWA-GATK-07. 6a-3. 1. 1 software on the hg19 version of the human genome.
Statistics Data were analyzed using Prism 8. 0. 2 (263) software (GraphPad, Insight Venture Partners, New York, NY). Binary and discrete variables are presented as numbers and percentages, with a 95% confidence interval (CI). Quantitative variables are presented as means for normally distributed data (verified using the Kolmogorov-Smirnov test) or as medians with interquartile range (Q1-Q3) for non-normally distributed data. The independence between two qualitative variables was tested using the chi-square test, whereas the independence between the quantitative variables was tested using the Mann-Whitney nonparametric test. All statistical tests were two tailed, with p values considered significant if below 0. 05.
Acknowledgments The authors are grateful to  Nikki Sabourin-Gibbs ,  CHU Rouen , for her help with editing the manuscript, and the staff of the  Department of Pathology  for their technical assistance, and especially  France Blanchard ,  Louison Leblond , and  Élodie Colasse. This work was mainly funded by  Boehringer Ingelheim  (" Bourse Régionale Profil Onco 2015 " awarded to  Nicolas Piton ).
ACKNOWLEDGEMENTS The authors acknowledge the technical assistance provided by the  Vanderbilt University Mouse Metabolic Phenotyping Center Analytical Core Services. Figure schematics were created using BioRender. com. GRANTS The  National Institute of Diabetes and Digestive and Kidney Diseases  Grants  DK050277  (DHW),  DK054902  (DHW), and  DK136772  (CCH) supported this research. The  Mouse Metabolic Phenotyping Center Analytical Core Services  receive support from  National Institute of Diabetes and Digestive and Kidney Diseases  Grants  DK059637  and  DK020593.
, 2019; Dyck et al. , 2017). PDE1 inhibitors have also demonstrated anti-parkinsonian effects, particularly potentiating Levodopa effect in rodent and monkey models of Parkinson's disease, as well as having potent efficacy against Levodopa induced dyskinesia  (Enomoto et al. , 2021). In a phase 1b research on PD in humans, a PDE1 inhibitor has shown therapeutic effects, and currently, Lenrispodun is in Phase 2 clinical trial for the treatment of Parkinson's disease (NCT05766813). Our data support the notion that PDE1 plays a role in the integration of calcium and cyclic nucleotide signaling in ChINs. In other types of neurons, PDE1 has been shown to control the calcium threshold for synaptic plasticity  (Kitagawa et al. , 2009) , but the precise physiological function of cyclic nucleotide and calcium signaling in ChINs is still unknown.
The estimates are computed from the history of recent measured inputs and outputs, and from a dataset of measurements prepared from experiments performed on the system under different operating conditions. The algorithm computes optimal error bounds on the estimated variable, yielding information on the estimation accuracy under different operating conditions. The problem structure is exploited to obtain a parallel algorithm implementation running on a graphics processing unit (GPU), and a dimensionality reduction technique is used to improve the execution performance. The resulting approach can be extended to full state observation by implementing separate estimators for each state variable. The paper is organized as follows: Section 2 presents the problem statement, Section 3 introduces the basic concepts of optimal filtering for state estimation and Section 4 presents the main contributions.
An example of this approach is presented in  [1]  where an adaptive control law is designed for the boost converter operating in DCM based on the computation of the switching times for the sequence of modes, assuming exact knowledge of the system model. When no uncontrolled switching events are present, the model in Eq. (  2 ) corresponds to the classical average model commonly used in the context of power converters. In the present approach, we have no knowledge of the system matrices of this model but instead assume that a dataset of input and output measurements is available. Problem 1. Consider the system in Eq. (  2 ). Assume matrices P, Ā, B, C, D, B x , B y are unknown. Based on discrete-time measurements of u, y and s, obtain discrete-time estimates x of the unmeasured state x.
A dataset D = { dk , ỹk , ũk , xk , k = 1, 2,. , N } is available, where xk = x k + ξ k is the noise corrupted measurement of x k , with ξ k the unknown but bounded measurement noise. Consider the filter xk = f ( φk ) where the regressor φk = [ dm k ; ỹm k ; ũm k ] corresponds to a column vector concatenation of the m most recent measured values { d, ỹ, ũ}. The learning process allows us to find an approximation f of the unknown optimal filter f 0 (see definition 2 in  [15] ) using the dataset D. Let us assume that the filter function f belongs to the set: F(γ ) = { f ∈ C 1 :   f ′ (ϕ)   ≤ γ , ∀ϕ ∈ Φ } (5) where f ′ is the gradient of f and Φ ⊆ R (n d +n y +n u )m is the regressor domain. Given a set of N samples of xi , φi , the feasible filter set (FFS) is defined as: F F S. = { f ∈ F(γ ) : ⏐ ⏐ xi -f ( φi ) ⏐ ⏐ ≤ ε, i = 1,.
, N }. (6) This set corresponds to the smallest set containing f 0 , or equivalently, the set of all systems consistent with prior information and measured data. The problem here consists of learning the values for parameters γ and ε such that the set F F S is not empty. The worst-case bounds for the estimation are given by: f ( φk ) = min i=1,. ,N ( xi + ε + γ   φk -φi   ) , (7a) f ( φk ) = max i=1,. ,N ( xi -ε -γ   φk -φi   ). ( 7b ) The following result is useful for checking the validity of a priori data: Theorem 1. 1. A necessary condition for the F F S to be non-empty is f ( φi ) ≥ xi -ε, i = 1,. , N. 2. A sufficient condition for the F F S to be non-empty is f ( φi ) > xi -ε, i = 1,. , N. Proof. See  [14]. □ Using these conditions, the values γ and ε can be learned from the training dataset.
Current estimation for power converters operating in CCM and DCM The approach introduced in the previous section is used to design a discrete-time estimator for average current in power converters operating in CCM and DCM. This approach does not require knowledge of the operation mode because the changes in dynamic behavior are encoded in the dataset when transitions occur between CCM and DCM. Let us assume that the averaged power converter model operating in CCM and DCM is represented by the discrete-time unknown model in Eq. (3), where x k is the state (inductor currents and capacitor voltages), d k the duty cycle of the PWM signals controlling the switching devices, u k the uncontrolled measured input voltage, y k the measured output voltage and x k an unmeasured unknown state variable.
Also, let us assume that the nonlinearities associated with CCM-DCM transitions are represented by the nonlinear functions F, G in Eq. (  3 ). Algorithm 1 specifies the offline procedure for learning the parameters γ and ϵ required to design a direct filter for current estimation in power converters operating in CCM and DCM. A sufficient number of experiments must be performed to prepare the dataset D. The design of these experiments requires special considerations to obtain a sufficiently informative dataset  [13]. Once a dataset D is available and the parameters γ and ε have been obtained, the filter estimates are computed online using algorithm 2. Algorithm 1: Direct filter design (offline) Result: D, γ , ε 1. Design random test signals d(t), ũ(t) for driving the power converter to operate under varied conditions (CCM and DCM) 2.
Measure ỹ(t), x(t). The sampling time should be sufficiently high to obtain detailed waveforms of the switching behavior 3. Compute ȳ(t) = average(ỹ(t)), x(t) = average( x(t)), with average(•) a non-causal filter 4. Obtain ỹk = resample(ȳ(t), T s ), xk = resample( x(t), T s ), dk = resample( d(t), T s ), ũk = resample( ũ(t), T s ), with T s a sample time sufficiently small to capture the low frequency behavior of the system 5. Prepare dataset   8 ); end D = { φi , xi , i = 1, 2,. , N } using dk , ỹk , ũk , xk 6. Take ε = ∥ x(t) -x(t)∥ ∞ 7. Take γ * = min γ , subject to f ( φi ) > xi -ε, i = 1,. ,.