dataesr/scientific-paragraphs-categorization huggingface dataset:
e. the q-value) was < 0. 01. The first 12 h of illumination (T12) saw very few significant changes in protein abundance (Supplemental Dataset 1). After 8 h of illumination (T8), we observed decreased abundance of only one protein (the photoreceptor cryptochrome 2, consistent with its photolabile property) and increased levels of only three proteins, which belonged to the chlorophyll a/b binding proteins category involved in photoprotection (AT1G44575 = PsbS; AT4G10340= Lhcb5; AT1G15820= Lhcb6;  (Chen et al. , 2018; Li et al. , 2000). A drastic change of proteome composition occurred by T24, with 467 proteins showing a significant increase in abundance with over 2-fold change (FC>2) compared with the etiolated stage, and 150 proteins showing a significant decrease with over 2-fold change (FC<0.
5). As expected, the 100 mostupregulated proteins comprised proteins related to photosynthesis, proteins constituting the core and antennae of photosystems, and proteins involved in carbon fixation (Supplemental Dataset 1). To monitor the dynamics of the plastidial proteome, we selected proteins predicted to localize to the plastid (consensus localization from SUBA4;  Hooper et al. , 2017). Generation of a global heatmap for each of the 1,112 potential plastidial proteins revealed different accumulation patterns (Supplemental Dataset 2 and Figure  5 -figure supplement 1). Hierarchical clustering showed a categorization into six main clusters. Cluster 1 (purple) contained proteins whose relative amounts decreased during de-etiolation. Clusters 2, 5, and 6 (pink, light green, and dark green, respectively) contained proteins whose relative amounts increased during deetiolation but differed with respect to the amplitude of variations.
Proteins in clusters 2 and 6 displayed the largest amplitude of differential accumulation. Gene ontology (GO) analysis  (Mi et al. , 2019)  indicated a statistically significant overrepresentation of proteins related to the light reactions of photosynthesis in clusters 2 and 6 (Supplemental Dataset 2). Underrepresentation of organic acid metabolism, in particular carboxylic acid metabolism, characterized cluster 2, whereas overrepresentation of carboxylic acid biosynthesis and underrepresentation of photosynthetic light reactions were clear features of cluster 3. Protein levels in cluster 3 changed only moderately during de-etiolation in contrast with proteins levels in cluster 2. No biological processes were significantly over-or underrepresented in clusters 1, 4, and 5. To analyse the dynamics of proteins related to thylakoid biogenesis, we selected specific proteins and represented their pattern of accumulation during de-etiolation (Figure  5 ).
Dynamics of chloroplast membrane lipids Total lipids were extracted from seedlings collected at different time points during de-etiolation (T0, T4, T8, T12, T24, T48, T72, and T96), analysed by ultra-high pressure liquid chromatography-mass spectrometry (UHPLC-MS), and quantified against pure standards (supplemental Dataset 3). We analysed the quantity and kinetics of accumulation of 12 different species of galactolipids (Figure  7A  and B ). MGDG 18:3/16:3, MGDG 18:3/18:3, MGDG 18:3/16:1, DGDG 18:3/18:3, and DGDG 18:3/16:0 were the most abundant lipids detected at all time points. Accumulation of all galactolipids increased upon de-etiolation; however, clustering analysis identified two distinct kinetic patterns. One group displayed a leap between T8 and T12, whereas the other group showed a more gradual increase during the deetiolation period (Figure  7C ).
Equation 1: / = / *  *  5   Quantitative data for MGDG, DGDG, PsbA, PetC, and PsaC (nmol/seedling) obtained from lipidomic and immunological analyses (Figures  6  and 7 ) were converted into number of molecules/seedling using the Avogadro constant (N). To calculate the surface area exposed to the stroma and account for the lipid double layer of the membrane, corresponding values of lipids (Figure  4A ) were divided by 2. In addition, the lipid values were corrected by subtracting the portion of lipids incorporated into the envelope rather than present in the thylakoids (Table  1, Table 2  and Supplemental Dataset 3 ). The surface area occupied by molecules of MGDG and DGDG, and that of PSII, Cyt b6f, and PSI photosynthetic complexes (nm 2 per molecule, corresponding to stroma-exposed surface) were retrieved from the literature (Table  3 ).
Superimposition of molecular and morphometric data We compared the values of thylakoid surface, as obtained with the model based on molecular data, with the values obtained from the morphometric analysis (Figure  9 ). The total thylakoid surface per seedling (S_thylakoid_morpho) was calculated by multiplying the thylakoid surface (S_thylakoid) per chloroplast obtained by morphometrics (Figure  4A ) by the number of chloroplasts (nb. cp) per cell (Figure  8A ) and the number of cells (nb. cells) per seedlings for each time point (t). We estimated cell number per seedling by measuring the total volume occupied by palisade and spongy cells in cotyledons (that corresponded to 50% of total cotyledon volume) (Figure  9 -figure supplement 2) and dividing this by the average cell volume quantified by Amira software (Figure  7B ).
Thylakoid surface increased 20-fold between T4 and T24. Remarkably, PSII maximum quantum yield (Fv/Fm) reached the maximal value (0. 8) by T14, independent of light intensity (Figure  1D  and Figure  1 -figure supplement 1). This shows that PSII assembly, and more globally assembly of the photosynthetic machinery, occurs simultaneously with thylakoid membrane formation and that photosynthesis is operational almost immediately upon greening. Our proteomic and lipidomic analyses suggest that chloroplast ultrastructural changes rely on specifically timed molecular changes. Proteomic analysis revealed the accumulation patterns of more than 5,000 unique proteins at eight time points during de-etiolation. These data provide information for plastid development and more widely on light-regulated developmental processes (Supplemental Dataset 1).
Our dataset is more exhaustive regarding temporal resolution and the number of unique proteins detected than that of previous reports on chloroplast differentiation and de-etiolation  (Bräutigam and Weber, 2009; Plöscher et al. , 2011; Reiland et al. , 2011; Wang et al. , 2006). Here, we focused on chloroplast-localized proteins, specifically on thylakoid membrane proteins. According to the SUBA4 localization consensus, 1,112 proteins were assigned to plastids, which covers about a third of the total plastid proteome  (Ferro et al. , 2003; Hooper et al. , 2017; Kleffmann et al. , 2007). Our data suggest that the reorganisation of pre-existing molecules rather than de novo synthesis is responsible for the major chloroplast ultrastructural changes that occur between T0 and T4. These results are consistent with other studies reporting only minor increases in protein accumulation and translation during initial chloroplast differentiation  (Dubreuil et al.
By contrast, during the later stages of de-etiolation (the chloroplast proliferation phase), the contribution of other compounds omitted in our model is obviously required to build up thylakoid surface. Our proteomics data (Figure  5 -figure supplement 1 and Dataset 2) revealed some proteins that increased between T24 and T96, such as the FtsH protease (AT2G30950). FtsH proteases have a critical function during thylakoid biogenesis. In Arabidopsis, they constitute a hetero-hexameric complex of four FtsH subunits, which is integrated in the thylakoid membrane  (Kato and Sakamoto, 2018). Although the FtsH complex surface area is unknown in Arabidopsis, it can be considered as a potential compound contributing to the thylakoid surface changes missing from our mathematical model. Other proteins, such as those involved in carotenoid biosynthesis (AT3G10230) or fatty acid metabolism (AT1G08640), also increased significantly after T24, implying that they contribute to the 'ɛ' factor.
SBF-SEM SBF-SEM was performed on Durcupan resin-embedded cotyledons representing the four deetiolation time points T0, T4, T24, and T96. Overview of the mesophyll tissue (≈600 images) and zoomed stacks of the chloroplasts (≈300 images) were acquired. Voxel size of T4 zoomed stacks: 3. 9 x 3. 9 x 50 nm; T24: 4. 7 x 4. 7 x 50 nm; T96: 5. 6 x 5. 6 x 50 nm. Voxel size for T0 overview: 9. 5 x 9. 5 x 100 nm; T4: 19. implemented by open-source software 3D Slicer  (Fedorov et al. , 2012)  and MeshLab  (Cignoni et al. , 2008)  respectively.
Segmentation, 3D reconstruction, and surface and volume quantification Segmentation and 3D reconstruction of 3View and confocal images were performed using Amira software (FEI Visualization Sciences Group). Specifically, prolamellar body, thylakoids, and envelope membranes as well as the cells were selected using a semi-automatic tool called Segmentation Editor. From the segmented images, triangulated 3D surfaces were created using Generate Surface package. Quantification of morphometric data (Area 3D and volume 3D) was acquired using Label Analysis package.
Chloroplast number determination Chloroplasts per cell were counted manually using Image J software (Wayne Rasband, National Institutes of Health). From the same SBF-SEM stack, 5 and/or 6 cells were cropped at each time point (T0, T4, T24, and T96) to quantify chloroplast number per cell. From TEM images, chloroplast number/cell was determined at T24 (16 cells), T48 (12 cells), T72 (12 cells), and T96 (17 cells). TEM images were acquired from two independent experiments.
Precursor masses previously selected for MS/MS measurement were excluded from further selection for 30 s, and the exclusion window was set at 10 ppm. The samples were acquired using internal lock mass calibration on m/z 371. 1012 and 445. 1200. The mass spectrometry proteomics data were handled using the local laboratory information management system (LIMS)  (Türker et al. , 2010). Protein quantification based on precursor signal intensity was performed using ProgenesisQI for  Proteomics (v4. 0. 6403. 35451; nonlinear dynamics, Waters). Raw MS files were loaded into ProgenesisQI and converted to mzln files. To select the alignment reference, a group of samples that had been measured in the middle of the run (to account for drifts in retention times) and derived from de-etiolation time point T12 or later (to account for increasing sample complexity) was preselected, from which replicate 3 of time point T48 was then automatically chosen as best alignment reference.
After automatic peak picking, precursor ions with charges other than 2+, 3+, or 4+ were discarded. The five highest-ranked MS/MS spectra, at most, for each peptide ion were exported, using the deisotoping and charge deconvolution option and limiting the fragment ion count to 200 peaks per MS/MS. The resulting Mascot generic file (. mgf) was searched with Mascot Server version 2. 6. 2 (www. matrixsicence. com) using the following settings: trypsin digest with up to two missed cleavages allowed; carbamidomethylation of cysteine as fixed modification; N-terminal acetylation and oxidation of methionine residue as variable modifications; precursor ion mass tolerance 10 ppm; fragment ion (MS/MS) tolerance 0. 04 kDa. This search was performed against a forward and reverse (decoy) Araport11 database that included common MS contaminants and iRT peptides.
The mascot result was imported into Scaffold Q+S (v4. 8. 9; Proteome Software Inc), where a spectrum report was created using a false discovery rate (FDR) of 10% and 0. 5% at the protein and peptide level, respectively, and a minimum of one identified peptide per protein. After loading the spectrum report into ProgenesisQI, samples were normalized using the "normalize to all proteins" default settings (i. e. normalization was performed to all ions with charges 2+, 3+ or 4+). Samples were grouped according to de-etiolation time point in a between-group analysis with 4 replicates for each condition, except for time point T0 and T48, where n = 3. For these two time points, one replicate each had been discarded it appeared as an outlier in principal component analysis (PCA) of protein abundances between different runs (Supplemental dataset 1).
Anti-FtsZ-1 and anti-FtsZ2-1/FtsZ 2-2  (El-Shami et al. , 2002; Karamoko et al. , 2011)  and were used at 1/2,000 dilution in 5% milk/TBS. After incubation with primary antibodies overnight at 4°C, blots were washed 3 times in TBS containing 0. 1% (v/v) Tween without antibodies for 10 minutes and incubated for 1 h at RT with horseradish peroxidase-conjugated secondary antibodies (1/3,000 (v/v) anti-rabbit or anti-mouse secondary antibodies, Agrisera). Chemiluminescence signals were generated with Enhanced chemiluminescence reagent (1 M Tris/HCl pH 8. 5, 90 mM coumaric acid, and 250 mM luminol) and detected with a Fujifilm Image -Quant LAS 4000 mini CCD (GE Healthcare). Quantifications were performed with ImageQuant TL software (GE Healthcare).
5 min. Flow rate was 0. 8 ml min -1 and the injection volume 2. 5 µl. Data were acquired using MassLynx version 4. 1 (Waters), and processed with MarkerLynx XS (Waters). Peak lists consisting of variables described by mass-to-charge ratio and retention time were generated  (Martinis et al. , 2011; Spicher et al. , 2016). Absolute quantification of mono-(MGDG) and di-galactosyldiacylglycerol (DGDG) was conducted by creating calibration curves using MGDG (reference number 840523) and DGDG (reference number 840523) products of Avanti Company. Calibration curves were prepared using the following concentrations: 0. 08, 0. 4, 2, 10, and 50 µg ml -1 of MGDG or DGDG.
Mathematical Model A non-linear mixed effects model (with fixed effect of time and random effect of replicates on 3 of the parameters), built on a 4-parameter logistic function, was implemented in R (free software created by Ross Ihaka and Robert Gentleman, Auckland University, New Zealand), following the examples in  Pinheiro and Bates (2000). The R-packages used are: nlme  (Pinheiro and Bates, 2000) , effects, lattice and car  (Fox and Weisberg, 2018). To account for self-correlation at the replicate level, we proceeded to fit an overall mixed-effects model to the data (package 'nlme' from R), using the replicate's as random effect term (Figure  9_supplement 1 ). The four parameters a, b, c and d have been calculated (Figure  9_supplement  1) and the three plots (one for each biological replicate) (Figure  9_supplement  1) indicated the fitting curve for a series of data points.
Quantification of thylakoid surface per chloroplast (A) and chloroplast volume (B) using 3-day-old, dark-grown Arabidopsis thaliana (Columbia) seedlings illuminated for 0 h, 4 h, 24 h, and 96 h in continuous white light (40 µmol/ m2/s). Morphometric data were quantified by Labels analysis module of Amira software. Error bars indicate ± SD (n=3). The total thylakoid surface indicated in A corresponds to the thylakoid surface exposed to the stroma, calculated in Amira software, in addition to the percentage of the grana surface (%Gs) calculated as described in Figure  3 -figure supplement 1. Thylakoid surface per seedling was estimated using quantitative data from 3View analysis ('MORPHO' black dots at T4, T24, and T96; and see Figure  4  and Table  1 ) and model generated using the quantitative data from proteomics and lipidomics ('MODEL' red line at T0, T4, T8, T12, T24, T48, T72, and T96, and Table  1 ).
3 x 19. 3 x 100 nm; T24: 40 x 40 x 200 nm; T96: 43. 5 x 43. 5 x 200 nm. Acquired datasets were aligned and smoothed respectively, using the plugins MultiStackReg and 3D median filter, provided by the open-source software Fiji. We performed a stack-reslice from Fiji to generate a new stack by reconstructing the slices at a new pixel depth to obtain isotropic voxel size and improve z-resolution. The segmentation and 3D mesh geometry information of plastid /thylakoid (T0, T4, T24 and T96) were.
Figure4: Quantitative analysis of chloroplast volume and thylakoid surface during de-etiolation. Quantification of thylakoid surface per chloroplast (A) and chloroplast volume (B) using 3-day-old, dark-grown Arabidopsis thaliana (Columbia) seedlings illuminated for 0 h, 4 h, 24 h, and 96 h in continuous white light (40 µmol/m2/s). Morphometric data were quantified by Labels analysis module of Amira software. Error bars indicate ± SD (n=3). The total thylakoid surface indicated in A corresponds to the thylakoid surface exposed to the stroma, calculated in Amira software, in addition to the percentage of the grana surface (%Gs) calculated as described in Figure3-figure supplement 1.
In the article, techniques complex of the transmission with IRBFC power parameters determination are considered. Moreover, software of automated calculation of the power parameters is created based on the techniques. The software consists of two parts: program of the mechanical transmission creating as a computer object; program of the graphical user interface creating. In general, the software makes it possible to determine the distribution of forces and contact stresses in engagement along the cycloidal profile of gear, to determine the maximum force and contact stress in engagement and to generate a file in excel format with all calculated data.
Making an automated calculation program The goal was to create a program that can be used on all types of computers with any operating system. This objective allowed choosing the computer language that would be used; in this case, the language was JAVA. On the other hand, this language requires the installation of java on the physical target (computer). This language is one of the most used in the computer world to create desktop applications because it is very powerful (belongs to object-oriented programming language). On Internet there are many resources and forums concerning this language such as "Stack overflow" or "Git Hub", which allows solving problems related to programming quickly. To realize the computer object, the software used was "Eclipse" which is one of the best known to develop in JAVA.
For the graphical interface, it was the software "NetBeans", which has a basic graphical interface designer. The program allows performing calculation of contact strength of details loaded for transmission with IRBFC. The software allows us to change quickly the input parameters, if necessary. The program was developed by equations (  1 )-(  4 ) for calculating the forces and contact stresses in the engagement of transmission with IRBFC, the interface of which is shown in figure  3. The interface of the calculation program (figure  3 ) explains the meaning of the entered parameters and the dimension in which they should be entered into the program is indicated by frame 1. In addition, the program provides the ability to set the material of gear wheels and rolling elements of the gear, this is located in the lower right part of the interface (figure  3 , frame 2).
One of the windows corresponds to the data by the cam wheel and the other window to the crown wheel. Moreover at the time of opening of these windows an excel file is created in parallel. This file contains all the values calculated and displayed by the windows. This file allows working directly with the exact values calculated by the program. The forces are expressed in Newtons and the contact stresses in Pascals. Since the accuracy of these data depends on the pitch of the angle  (which varies from 0° to 180°), the chosen pitch is 0. 5° which allows obtaining a good precision. Finally, we can see the data displayed in a drop-down table, as well as the maximum value of force and contact stress. The creation of this computer program is unrolled in 2 parts: -> the first part consisted of creating a simple program that performed all the calculations related to the mechanical system, and creating the mechanical transmission as a computer object (corresponds to a "class").
Description of the Experimental Test Bench To analyze the Faraday's efficiency versus the current and gas pressure change, an experimental test rig has been realized at the GREEN laboratory (IUT de Longwy, France) as shown in Figure  4. The test bench includes the following devices: (1) PEM electrolyzer, (2) H2 flow rate meter from Bronkhorst Company, (3) a current clamp, (4) a voltage probe, (5) 4-channel oscilloscope, (6) DC power supply from EA Elektro-Automatik Company, (7) a laptop with a virtual control panel to control the DC power supply, and (8) a pressurized pure water tank. The generated hydrogen is stored in three metal hydride tanks (not shown in Figure  4 ). The investigated PEM  The hydrogen delivery output pressure can be set through the software from HELIOCENTRIS Company with an RS-232 connection.
Mathematical Modeling of the Faraday's Efficiency Based on the results reported in Table  2 , the authors propose for the first time a fit empirical equation to model the Faraday's efficiency versus the current and hydrogen pressure change. The model has been devised considering the current density multiplied by a quantity depending on the pressure. To take into consideration nonlinear behavior, the current density has an exponent to be determined based on the pressure. First, by using the experimental data (i. e. , current density, Faraday's efficiency), the most suitable empirical expression has been found by using the curve-fitting tool of Matlab/Simulink (2020a, MathWorks, Natick, Massachusetts, USA) software. The empirical expression is based on a power equation including two terms as follows: η F = a i el A b + c ( 10 ) where A is the cross section of the studied PEM electrolyzer (m 2 ) and a, b, and c are parameters to evaluate.
From Figure  6 , parameter a varies linearly according to the hydrogen pressure. The higher the pressure, the smaller the parameter a. Thus, it can be modeled as a linear expression: By using the command polyfit of Matlab/Simulink software and the data given in Table  2 , the parameters a1 and a2 have been determined and are given in Table  4. By using the command polyfit of Matlab/Simulink software and the data given in Table  2 , the parameters a 1 and a 2 have been determined and are given in Table  4. In summary, by substituting Equation  (11)  into Equation  (10) , the complete empirical model can be obtained: = + (11) η F = (a 1 p + a 2 ) i el A b + c (12).
Acknowledgments: The authors are very grateful to the  French Embassy in Bangkok (Thailand) and Campus France  in funding  Burin Yodwong's Ph. D. Thesis  within the framework of the  Franco-Thai scholarship program. Furthermore, the authors would like to address a special thanks to the  GREEN laboratory of the Université de Lorraine and Thai-French Innovation Institute of King Mongkut's University of Technology North Bangkok  for their constant encouragement and support in promoting research collaboration between France and Thailand. This work was supported by  King Mongkut's University of Technology North Bangkok. Contract no. KMUTNB-GOV-58-58.
LTTT The thermal cycling device "LTTT" is comprised of a thermostatic bath (water or thermal oil) with a 4-channel PT100 data logger (expandable). Pictures of the device are shown in Figure  15. As temperature profile, one temperature level above and one the below phase-change temperature with a rapid change between the temperature levels or with defined heating/cooling rates are applied. Up to 16 samples can be cycled simultaneously in test tubes (made of glass, metal, or plastic), which are sealed with a rubber plug and immersed in the thermostatic bath. During the experiments, temperature-time curves of the samples are measured. After a certain number of cycles, DSC measurements are performed, and the temperature curves of the samples are analyzed. UDL-GREiA I (This Equipment Is Shared with the University of Barcelona, DIOPMA Research Group) The GeneQ BIOER TC-18/H(b) thermal cycler allows performing thermal cycles from room temperature up to 100 °C to investigate the stability of PCM samples over time.