dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability statement Data associated with this study are available in nine Supplementary tables. Transcriptomics datasets and digital histological slides are publicly available from the Gene Expression Omnibus (GEO-NCBI), TCGA, and the Digital Slide Archive website. The MASLD meta-dataset results from the pretreatment, merging, and batch effect correction of raw transcriptomic data from publicly available datasets from different technical platforms. Meta-dataset construction has been previously described. 28  The merged user-friendly dataset can be made available from the authors upon request.
Acknowledgement This work was supported by  Agence Nationale de la Recherche  " PHySIOMIC "  ANR-20-CE19-0031  (to T. B. and A. P. ); " FLAMRING "  ANR-20-CE19-0032-01  (to S. M. d. L. ); the  Region Normandie  under the convention  RIN Doctorant  project Modirem (to C. J. ); and " Fondation Bettencourt Schueller " for a  CCA-Inserm-Bettencourt  position (to M. G. ). Competing interests: The authors declare the following p. 28.
In addition, performing DTI on SC is technically challenging mainly due to the small dimensions of the SC, the physiological motion (e. g. , heart, lungs, and throat in its proximity) and the susceptibility-induced distortions. However as described below, the last decade has seen numerous developments in newer pulse sequences coupled with spine specific post processing capabilities to overcome these barriers enabling reproducible and reliable data collection of the human spinal cord. DTI is often performed using the single-shot Echo Planar Imaging pulse sequence (SS-EPI) due to its fast acquisition speed. However, this acquisition method is prone to various limitations, including eddycurrents and high-sensitivity to magnetic field inhomogeneity, inducing artifacts dramatically affecting the image quality.
In addition, this can be due to the high B0 field inhomogeneity in proximity to the metal, specifically at ultra-high field (UHF) such as 3 or 7 Tesla. In this study, we combine the rFOV technique with the PS-EPI pulse sequence, so called (rFOV-PS-EPI), to address geometric distortions near the metal in DTI scan of SC at 3 T. Distortion-reduced DTI data were collected on a custom-built cervical spine phantom model with a metal implant configuration commonly used in spine surgery. The efficacy of the proposed pulse sequence in reduction of metal artifacts was visually and quantitatively evaluated compared to the gold standard rFOV-SS-EPI pulse sequence as well as the full-FOV approaches: SS-EPI, PS-EPI, and RS-EPI.
Protocol setup The experiments in this study were conducted on 3 T Magnetom Prisma system (Siemens Healthineers, Erlangen, Germany) with a maximum gradient amplitude of 80 mT/m and a maximum slew-rate of 200 T/m/s. The phantom was placed in the vendor 64-channel receive head-neck coil, with the MR body coil used for radiofrequency (RF) transmission. In this phantom-based study, no human or animal subjects were involved in phantom construction or data collected. Therefore, approval from the ethics committee was not required.
Then, the 2DRF excitation pulse was implemented into the developed PS-EPI sequence to create the proposed rFOV-PS-EPI approach. The diagram of the rFOV-PS-EPI pulse sequence used for data collection is shown in Fig. 3. The 2DRF excitation pulse was used to acquire a rFOV along the Phase Encoding (PE) direction. It consists of 45 PE lines with an echo spacing of 0. 37 ms, resulting in a total duration of 16. 93 ms. The conventional Stejskal-Tanner diffusion preparation scheme with one refusing pulse of 180 • is used to acquire diffusion weighted images  [24]. Although the 2DRF aims to acquire a reduced number of acquired lines at each RF pulse, the Phase segmentation sampling scheme allows achieving a significantly shorter EPI readout train, addressing the susceptibility-induced geometric distortions.
SNR measurement A Region-Of-Interest (ROI)-based SNR measurement was performed on b0-images collected using the rFOV-PS-EPI and rFOV-SS-EPI to quantify the SNR improvement between the two pulse sequences. The ROIs were manually selected at the center of the asparagus using the Medical Image Processing, Analysis, and Visualization (MIPAV) software (mipav. cit. nih. gov). The measurement was not performed at the first and last slices to avoid effects from signal dropout induced by RF pulse imperfection. Slices 10 to 18 were excluding from measurement due to the presence of high-intensity geometric distortions in data acquired with the standard rFOV-SS-EPI pulse sequence.
8. The slice-wise ROI-parameters extracted from the selected ROI: Area, Perimeter, Circularity, and Eccentricity. Great affinity between the rFOV-PS-EPI and the reference T2 metrics, demonstrating the capacity of the developed technique to provide distortion-reduced DTI images. In addition, no measurements were extracted from data collected using the rFOV-SS-EPI method at the site of the Hardware due to the presence of signal void and high-intensity geometric distortions.
Materials and methods The computer-aided process design package Aspen One™. (v. 11) is the main tool used in this work. Its capabilities include an accurate description of physical properties, rigorous models for unit operations and numerical techniques for solving large systems of algebraic and differential equation. To model reactors and stacks, Aspen Customed Modeler ™ (ACM) is utilized, enabling straightforward integration with the Aspen Plus ™ architecture as a customized component. In all simulations, the Peng-Robinson cubic equation of state  [31]  is selected to evaluate the thermodynamic properties of all substances which is consistent with other similar studies  [32, 33].
Optimization of the different systems To facilitate a fair comparison between the three systems, the Aspen Plus software SQRP (Successive Quadratic Programming) was utilized to optimize the parameters of the flowsheet structure. This optimization process involved finetuning the operating conditions, such as permeate pressure, reactor inlet pressure, flow rate, and temperature, as well as sizing parameters, including membrane area, for a total of five parameters. The primary goal was to maximize the fuel processor efficiency h th , while still satisfying specific constraints, such as the addition of air to the burner to attain an O 2 content of 5% (on a molar basis), maintaining the required hydrogen productivity (i. e. 0. 84 kmol/h), and ensuring that the fluidized bed reactor remains in the "bubbling bed regime" with a u/u mf ratio between 2 and 10.
Conclusions The objective of this study was to carry out an initial technoeconomic evaluation of PEMFC genset systems that integrate membrane reactors as fuel processors, utilizing various feedstocks such as methane, ammonia, and methanol for storing significant amounts of renewable energy in the powerto-X framework. To accomplish this objective, the study employed a computer-based process simulation approach utilizing Aspen Plus™ software and utilized a two-stage optimization method. Initially, a sensitivity analysis was conducted to examine the influence of different operating conditions, such as reactor temperature, pressure, and permeate-side pressure on the overall efficiency of the fuel processors. Subsequently, a parameter optimization was performed to facilitate a fair comparison of the three feedstocks hydrogen and screen quickly through the variety of combination which can arise (electrical or thermal furnace, storage conditions of the methane).
Similarly, varying in cases, intravenous AC (60 mg/m 2 + 600 mg/m 2 ) was administered in each of the 4 cycles. The WBCs counts of the subjects affected by both TC and AC combinations were observed for comparison. An equation was derived to calculate the effect of TC and AC on WBCs count (eWBCc). The eWBCc of 171 patients who received TC and 154 patients who received AC were calculated by implantation of the derived equation using Python 3. 8. Results: Comparing the results of 171 patients, it is observed that, TC affected WBC count of a patient at lowest level by 12. 29%, at maximum level by 69. 64%, and on average by 34. 13%. Whereas, comparing the results of 154 patients, AC affected WBC count of a patient at lowest level by 19%, at maximum level by 65. 03%, and on average, by 44. 36%. A paired t-test was used to analyze the statistical differences between the eWBCc of both TC and AC cohorts.
Study design In this study, women with breast cancer treated chemotherapy in a governmental hospital were selected to observe the effect of TC and AC on their WBCs counts during four cycles of chemotherapy with both combinations. This work was permitted (approval number 675/2016) by the ethical review committee, in Faisalabad, Pakistan. All patients provided written informed consent for the use of their white WBCs. Data from 668 breast cancer patients treated with chemotherapy were collected. After applying the exclusion and inclusion criteria (see the next subsection), we examined 459 cases in our records. Out of the 459, 134 subjects were excluded from the study because they were being treated with other drugs including tamoxifen, paclitaxel, and epirubicin. Thus, 171 of the remaining 325 patients were treated or were being treated for four cycles of chemotherapy with TC, and 154 women with breast cancer received or were being treated for the next four cycles of chemotherapy with AC.
Effect of TC and AC on patient's WBCs count Python 3. 8 was used to implement Eq. (  2 ) providing a WBCs count of 154 patients during four cycles of chemotherapy with AC and a WBCs count of 171 patients during four cycles of chemotherapy with TC. Tables 2 and 3 show eWBCc values for AC and TC patients, respectively. From Table  2 , it is observed that comparing the results of 154 patients obtained from the implementation of Eq. (  2 ), AC affects WBC count of patient number 110 at the lowest level is 19 %. However, at the maximum level, in these patients, the WBC count of patient number 84 was affected 65. 03 % by AC. However, on average, AC remains toxic, affecting these patients WBC count of 44. 36 %. Interviewing patient no. 110, it was found that the food and environment provided to this patient collectively played a vital role in controlling his WBC count.
As previously observed during mitotic exit, the proteome is mainly stable during meiosis I exit, too (Figures  S1B-D )  (Touati et al. , 2018). Indeed, less than 3% of the proteome fluctuated in our main dataset. Consequently, most changes in the phosphoproteome are expected to be phosphorylation-dependent. The phosphoproteome is a lot more dynamic than the proteome, with similar number of phosphosites (14%-17%) lost or gained phosphorylation during the time course. Still, the majority of the phosphoproteome remained stable during meiosis I exit (Figure  1C ). The number of phosphosites and the overlap between the two repeats are summarized in Figure  S1B  and S1E. To globally visualize the phosphorylation dynamics during meiosis I exit, we performed unsupervised hierarchical clustering (Figure  1D ).
, 2019). Examples of phosphosites dephosphorylated or phosphorylated the earliest or latest are shown in Figure  S1F , S1G, S1H and S1I respectively. Many proteins implicated in the synaptonemal complex are dephosphorylated before anaphase I (Figure  S1F ). Many cell cycle regulators start their dephosphorylation during anaphase I and some of them peak in phosphorylation before being dephosphorylated (Figures  S1G-H ). Finally, we identified a last cluster of proteins whose phosphorylation starts only when cells enter meiosis II (Figure  S1I ). Altogether, our dataset allows us to capture subtle changes in phosphorylation dynamics and to highlight the timing of phosphorylation abundance change during the meiotic cell cycle progression.
Distinct phosphoregulation of substrates specific for mitotic or meiosis I exit We previously generated a mitotic time-resolved phosphoproteome covering events from metaphase to G1, using the GALpromoter-CDC20 block and release system and taking time points every 5 min for 45 min  (Touati et al. , 2018). We wanted to compare how the phosphoproteome varies between mitotic and meiosis I exit. To do so, we reanalysed the main dataset of  (Touati et al. , 2018)  together with the new meiotic dataset (Figure  1 ). At first glance, mitotic and meiosis I exit appear similar regarding phosphosite numbers, and overall dynamics. Indeed, 31% of phosphosites were either dephosphorylated or phosphorylated and 69% were stable. However, dephosphorylation seems to be predominant only during mitotic, and not meiotic, exit (Figure  2A ).
Examples of phosphosite profiles are shown in Figure  S2A. During mitotic exit, substrates are strongly dephosphorylated at the cellular bud neck, and this is not observed during meiosis I exit (where the bud neck is absent). Finally, we found spindle pole body substrates principally phosphorylated during meiosis I exit, while they tend to be rather dephosphorylated during mitotic exit (Figure  2B ). Specific examples of spindle pole body phosphosites identified in the two datasets are shown in Figure  S2B. This result suggests that some specific compartments have a kinase/phosphatase ratio that is different between mitotic and meiosis I exit. Altogether, this makes sense for the spindle pole body, which needs to be tightly regulated to allow a second round of chromosome segregation without interfering with the DNA replication program at the MI-MII transition.
However, phosphoresidues with basic residues (K/R) in +1-4 positions in mitosis and meiosis are less frequently phosphorylated (Figure  S2C ). The phosphoenrichment technique used may increase the affinity for some phosphomotifs over others. However, we think this is unlikely to generate a bias in our analysis, as all our peptides were mixed before being phosphoenriched on the same column. Also, we do not just determine whether a phosphosite is phosphorylated, but rather if phosphorylation on a specific phosphosite increases or decreases overtime. After normalization according to the number of each phosphomotif identified in the two datasets we analyzed if the position of basic or acidic residues impacts the behavior of a phosphosite during exit (i. e. , being dephosphorylated or phosphorylated).
Full Cdk sites dephosphorylated during mitotic exit are mostly stably phosphorylated during meiosis I exit Dephosphorylations are critical to reset the cell cycle during mitotic exit, but whether a partial reset also occurs during meiosis I exit remained still elusive. To answer this question, we performed a direct comparison of phosphosite behavior during mitotic and meiosis I exit. To do so, we analyzed the 727 phosphosites that are in common between the two datasets (Figure  3A ). The Sankey diagram shows that the majority of phosphosites that remain stable during mitotic exit also remain stable during meiosis I exit. Next, we focused our attention on phosphosites that are reset by dephosphorylation during mitotic exit and uncovered three categories of meiotic phosphosubstrates with distinct phosphorylation dynamics.
We plotted the profiles of the 31 full Cdk motifs that are dephosphorylated during mitotic exit and that are also present in the meiosis I exit dataset, highlighting well-known cell cycle regulators. Some of the sites undergo subtle variations at the MI-MII transition but overall, they remain stably phosphorylated during meiosis I exit (Figure  3D ). Altogether, our results demonstrate that phosphorylations by the master Cdk cell cycle regulator are reset during mitotic exit, but not during meiosis I exit, thus highlighting a key difference in protein phosphorylation landscape during mitosis and meiosis I exit.
Time-resolved phosphoproteome analysis reveals theoretical phosphatase activity profiles Phosphatases also harbor substrate specificity. In yeast and mammals, PP2A Cdc55 and PP1 were shown to preferentially dephosphorylate threonine motifs while Cdc14 prefers full serine Cdk sites  (Baro et al, 2018; Bremmer et al. , 2012; Godfrey et al, 2017; Hoermann et al. , 2020; Kruse et al. , 2020; Kuilman et al. , 2015; Powers & Hall, 2017; Touati et al. , 2019). We previously showed that Cdc14, PP2A Cdc55 and PP2A Rts1 cooperate to promote substrate dephosphorylation during mitotic exit  (Touati et al. , 2019). The modulation of phosphatase activity during meiosis I and II could be the reason why full Cdk threonine motifs are not dephosphorylated, so we decided to search for a read-out of their activities in our dataset.
In our meiotic dataset, the majority of SPx(K/R) is poorly dephosphorylated, indicating that indeed, Cdc14 is not fully active (Figure  4I )  (Touati et al. , 2019; Touati et al. , 2018). Thus, when comparing the mitotic and meiosis I exit datasets, the phosphorylation balance is ascending during meiosis I exit as Cdk activity is only partially reduced  (Carlile & Amon, 2008)  and Cdc14 is only partially active. Thus, full Cdk motifs are barely dephosphorylated at meiosis I exit, especially the TPx(K/R), which are less favored by Cdc14.
Repeat A had the highest range of decrease and repeat B the lowest (Figure  5F ), which can explain why cells managed to perform anaphase I more easily in repeat B of Figure  S4A. Note that overall, the minimal Cdk motifs were not decreased after 3-MB-PP1 treatment, which confirms that very few (S/T)P motifs are direct targets of Cdk (Figure  S4E ). Only phosphosites following the same behavior in at least two datasets were used for global IceLogo motif analysis to determine which category of phosphomotifs is most affected by Cdk decrease. We noticed that (S/T)PxK sites were more efficiently dephosphorylated than (S/T)PxR (Figure  5G ). This result is consistent with in vitro phosphopeptide analysis  (Suzuki et al. , 2015). Thus, the presence of a K or R downstream could be a strategy employed by the cell to modulate phosphorylation levels of some substrates.
Mass Spectrometry data analysis of meiosis I exit datasets MaxQuant (version 1. 6. 17) was used for all data processing. The data was searched against a UniProt extracted Saccharomyces cerevisiae proteome FASTA file (downloaded on April 2022 as FASTA file), amended to include common contaminants. Default MaxQuant parameters were used with the following settings: fixed modifications: carbamidomethyl (C), variable modifications: oxidation (M), acetyl (protein N-term), phosphorylation (STY), and deamidation (NQ); enzyme: Trypsin/P; max. missed cleavage: 2; first search peptide tolerance: 20 ppm; main search peptide tolerance: 4. 5 ppm; second peptide search was enabled. All other settings were set to default. Results were filtered by 1% FDR on PSM and protein level. MaxQuant output files were imported into Perseus (version 1.
Analysis of phosphosite abundance change The mitotic dataset was  (Touati et al. , 2018)  reanalyzed using the same parameters for the mitotic exit and the meiosis I exit dataset (Figure  1 -4 and S1-3). Phosphosite intensities were normalized to 1 at time point 50min for the meiosis I exit dataset (time when the majority of cells are in metaphase I) and at time point 0min for the mitotic exit dataset (time of metaphase release). All the analyses (except the ones mentioned in the figure legend) are in linear scale. A 1. 5-fold decrease in phosphorylation abundance, or a 1. 5-fold increase, over at least two consecutive time points was required to meet classification as "dephosphorylated" or "phosphorylated", respectively. Phosphosites that showed a less than 1. 5-fold change were considered stable.
For 3-MB-PP1 experiments (Figure  5 -6, S4-5), the ratio of 3-MB-PP1 data and control data was performed at each time point. A 2-fold decrease in phosphorylation abundance, or a 2-fold increase, over at least two consecutive time points was required to meet classification as "decrease" or "increase", respectively. Phosphosites that showed a less than 2-fold change were considered stable. For figures where more than one 3-MB-PP1 repeat is presented, datasets were normalized by subtracting the median of the log2 transformed values to obtain the same range of phosphorylation between the 3 datasets. In these conditions, a 1. 5 cut off was used.
IceLogo sequence analysis Amino acid distributions surrounding phosphosites was analyzed using IceLogo https://iomics. ugent. be/icelogoserver/  (Colaert et al, 2009). Phosphoresidues are placed at the central position within the sequence logo. A p-value between 0. 05 or 0. 001 was applied as the detection threshold when using IceLogo. Percentage difference reflects the frequency of an amino acid in the indicated category, at a given position, compared to its frequency in the whole or the part of the dataset, from where the category had been extracted.
Acknowledgements We thank  L. Koch  and  A. Marston  for discussion of unpublished results prior to submission, and  Frank Uhlmann  for comments on the manuscript. We would like to thank  Folkert van Werven  for Saccharomyces cerevisiae strains,  Emilie-Fleur Gautier ,  Cédric Broussard  and  Marjorie Leduc , from  Plateforme Proteom'IC 3P5, Université Paris Cité, Institut Cochin, Paris, France  for performing sample acquisitions, and team members for discussion and critical reading of the manuscript. SAT received funding for this work by the  Agence Nationale de la Recherche  ( ANR-21-CE13-0026 ), the  Fondation de la Recherche contre le Cancer  ( ARCPJA12020060002043 ), the  CNRS  and  Tremplin Sorbonne Université  2020.
Phylogenetic analysis The amino acid sequences of GPERs from different vertebrate species used for alignments and phylogenetic analysis were extracted from the Ensembl Genome Browser (http://www. ensembl. org; 02/2022) or from NCBI (http://blast. ncbi. nlm. nih. gov; 02/2022) and the accession numbers can be found in Supplementary Table  S1. The original designations used for each sequence were maintained in the table and phylogenetic tree, while the sea bass Gper forms were designated as Gper1 and Gper1-like according to their position in the phylogenetic tree (see results). Following established organism specific nomenclature rules for genes/proteins the notations GPER/GPER were used for tetrapods while fish forms were designated as gper/Gper. Complete amino acid sequences were aligned using MUSCLE (MUltiple Sequence Comparison by Log-Expectation;  [46] ), and phylogenetic analysis was performed using the Molecular Evolutionary Genetics Analysis Software (MEGA 11;  [47] ).
Expression plasmids The coding sequences of sea bass Gpera (renamed as sbsGper1) and Gperb (renamed as sbsGper1-like) with accession numbers MF508726 and MF508727, respectively, were amplified by PCR with iProof High-Fidelity proofreading DNA Polymerase (Bio-Rad, Hercules, USA), using previously isolated full-length transcripts of the respective gpers as templates  [11]. Specific primers were modified to include restriction enzymes compatible with the pcDNA3. 1/V5-His TOPO expression vector (Invitrogen, Carlsbad, USA). To isolate the 1065-bp coding sequence of sbsGper1, the primers used were sbsGPERxpF1 (5′-GCGAAGCTTATTATGGAAGTGCAGAC-3′) and sbsGPERxpR1 (5′-CGCGCGGCCGCTTCACACCTCTGACAC-3′) including the recognition sites for HindIII and NotI, respectively. For 1215-bp coding-sequence of sbsGper1-like, the primers used were sbsGPERLxp F1 (5′-GCGAAGCTTATGGAGAATCACTTGTC-3′) and sbsGPERLxp R2 (5′-CGCGCGGCCGCTTAAATAGGTGAAGA-3′), also containing restriction sites for HindIII and NotI.
Statistics Data are presented as mean ± SEM of relative luciferase activity values, which were statistically analysed using one-way ANOVA followed by the Tukey pairwise multiple comparison method, in GraphPad Prism 8 (GraphPad software, Inc). When the test of equal variance failed (Bartlett's test), ANOVA on ranks (Kruskal-Wallis non-parametric test) was performed followed by a pair-wise multiple comparison procedure (i. e. , the Dunn's test). The significance level was set at a P value of ≤ 0. 05.