dataesr/scientific-paragraphs-categorization huggingface dataset:
39, +0. 15 and +0. 28 in White, Afro-Caribbean and Asian ethnicity respectively, this did not improve the model fit (Supplementary File 1, Table  1 ). Further analyses were carried out to investigate the effects of concomitant medications on CL/F clozapine in the larger dataset. Although the predictions were in the expected direction (see Supplementary File 1, Table  2 ), IIV in CL/F clozapine increased from 82 to 98%, with no reduction in residual unexplained variability.
33 Our estimate of the effect of age on clozapine dosing is higher than in our recently published audit, based on data from a much larger sample from the TDM dataset that predicted a 1. 7% decrease in clozapine dose requirements for every 5 years above 40 years. 35 It is, however, consistent with the 4% decrease in dose for every 5 years above 40 years reported by Rostami-Hodjegan and colleagues. 36.
The finding that dose requirements were increased in Afro-Caribbean patients (Table  3 ) is in line with the suggestion that dose requirements may be higher in those of African ancestry. 15 Pharmacokinetic studies of olanzapine (also a CYP1A2 substrate) have reported faster clearance in males, smokers and African American patients who participated in the Clinical AntipsychoTic Intervention Effectiveness (CATIE) study. 41 In a subsequent analysis of this latter dataset, CYP3A43 genotype rs472660 AA, which is expressed more frequently in those of African ancestry, was associated with faster olanzapine clearance; African ethnicity was no longer significant when this genotype was incorporated in the model. 42 These findings have questioned the relative contribution of CYP3A4 in clozapine metabolism.
43  the absence of genetic data, it is unclear to what extent CYP1A2 genotype or environmental factors, including diet, account for the observed ethnic group differences in our analysis. For example, vegetables such as cabbages, cauliflower and broccoli increase CYP1A2 activity  44  and spices that are commonly used in Asian cuisine, such as curcumin (the active component of turmeric) inhibit CYP1A2 activity. 45 It will be important to investigate this in future studies. ) and slope (percentage) parameters; ne, not estimated. F norclozapine (624 L, IIV 75. 6%). 23 e large size and wide age range of individuals in our TDM dataset allowed estimation of pharmacokinetic parameters and covariate effects. 20 Model-based estimates for CL/F clozapine (20. 2 L h À1 ) are consistent with published models, but V/F clozapine was higher (1460 L) than has previously been reported.
| Limitations and sources of unexplained variability This analysis has addressed some of the limitations described in relation to TDM data  48   were excluded from the analysis, it is likely that the dataset was incomplete in terms of concomitant medications. Secondly, there was limited information on other factors that could have influenced clozapine metabolism, including caffeine intake, diet, oral contraceptive or hormone replacement therapy use and the presence of inflammation, or of hepatic dysfunction. 16, 17, 48, 49  is likely that a proportion of the samples studied here were obtained from patients who were not fully adherent with treatment. Thus, it is possible that plasma clozapine and norclozapine concentrations were underestimated in relation to the prescribed dose in some individuals.
Model-based simulationModel-based estimates of V/F clozapine , CL/F clozapine and k m and IIV of these estimates were used to predict the average dose and its 90% prediction interval in the population, by sampling 100 individuals in the parameter population distribution and using uniroot to estimate their target doses, for the following: male/female, smoker/nonsmoker, White, Afro-Caribbean or Asian ethnicity, aged 40 and 80 years. Dose calculations were rounded to the nearest 25 mg to reflect real world prescribing. 3 | RESULTS3. 1 | Base and final pharmacokinetic modelOf 21 761 samples from 7451 patients extracted from the dataset, the following were excluded on the basis of concomitant medication (n = 3466; 52 fluvoxamine, 56 fluoxetine, 588 paroxetine, 1840 sodium valproate, 835 omeprazole, 35 phenytoin, 60 carbamazepine), mixed ethnicity (n = 377), or plasma clozapine below the limit of assay quantification (n = 131).
date and time of sample, and other relevant information on covariates were available. Moreover, selective analytical methodology quality controlled to international standards was used throughout. It could be argued that clinicians may have been more likely to specify the time since last dose for samples that were not taken predose and this is a possible source of bias in the dataset. This was, however, accounted for in the analysis, as the pharmacokinetic model takes into account time since last dose when exploring dose-concentration relationships and covariate effects. Model-based estimates are then used to simulate and predict predose concentrations. Although those prescribed either potent CPY1A2 inducers (phenytoin, carbamazepine, phenobarbital), or inhibitors (fluvoxamine).
ACKNOWLEDGEMENTS Novartis for help in establishing the clozapine TDM service,  Mr Harley Quilliam  for setting up the database used,  Dr Anwar Alhaq  ( King's College Hospital ) for facilitating database and statistician support, the healthcare professionals who requested clozapine TDM and filled in the associated forms, and  Prof Gerome Breen  ( King's College London ) for assistance. RH and SR are supported by the  NIHR University College London Hospital Biomedical Research Centre (UCLH BRC). We would also like to thank the reviewers for their feedback and guidance.
4 | 4 Model-based simulationModel-based estimates of V/F clozapine , CL/F clozapine and k m and IIV of these estimates were used to predict the average dose and its 90% prediction interval in the population, by sampling 100 individuals in the parameter population distribution and using uniroot to estimate their target doses, for the following: male/female, smoker/nonsmoker, White, Afro-Caribbean or Asian ethnicity, aged 40 and 80 years. Dose calculations were rounded to the nearest 25 mg to reflect real world prescribing. 3 | RESULTS3. 1 | Base and final pharmacokinetic modelOf 21 761 samples from 7451 patients extracted from the dataset, the following were excluded on the basis of concomitant medication (n = 3466; 52 fluvoxamine, 56 fluoxetine, 588 paroxetine, 1840 sodium valproate, 835 omeprazole, 35 phenytoin, 60 carbamazepine), mixed ethnicity (n = 377), or plasma clozapine below the limit of assay quantification (n = 131).
(G-I) Relationship between spindle properties and the augmin nucleation rate (/s). All temporal means are taken over the last half of the simulation, 500s < t < 1000s. (G) Mean spindle length (μm) against augmin nucleation rate (/s). The spindle length is measured as the distance between the center-of-masses of the left and right groups of condensates. (H) Mean lengths (μm) of each group of fibers nucleated at kinetochores (green circles), by augmin (yellow triangles), and at poles (purple squares). (I) Mean number of fibers of each type. (J-L) Relationships between kinetochore-fiber (k-fiber) properties and the spindle length (μm), with data points colored according to the augmin nucleation rate (/s). All quantities are means over the last half of the simulation, 500s < t < 1000s, and k-fiber quantities are averaged over all k-fibers.
(J) Mean k-fiber length (μm), (K) mean growth (μm) at k-fiber plus ends, and (L) mean severing (μm) at k-fiber minus ends. (M-O) Relationship between spindle properties and the augmin diffusion coefficient (μm 2 /s). All temporal means are taken over the last half of the simulation, 500s < t < 1000s. (M) Mean spindle length (μm) against augmin diffusion coefficient (μm 2 /s). The spindle length is measured as the distance between the center-of-masses of the left and right groups of condensates. (N) Mean lengths (μm) of each group of fibers nucleated at kinetochores (green circles), by augmin (yellow triangles), and at poles (purple squares). (O) Mean number of fibers of each type. (P-R) Relationships between kinetochore-fiber (k-fiber) properties and the spindle length (μm), with data points colored according to the augmin diffusion coefficient (μm 2 /s).
Arabidopsis root mitotic spindle simulation Mitotic spindles were simulated using Cytosim, an Open-Source project (gitlab. com/fnedelec/cytosim). Here, we provide an overview of our methods which are based on Brownian dynamics. The numerical aspects (integration, stability) were described previously  18. Further to this publication, accessibility of the source code should enable the full analysis of our methods, and reproducibility of the results. In brief, microtubules are modeled as incompressible bendable filaments having the persistence length of microtubules, in a medium characterized by a viscosity as measured for cells  48. Microtubules are represented by vertices distributed regularly along their length. For pathway N1 (kinetochores), each kinetochore harbors 5 nucleators, and their nucleation rate is fixed and unregulated.
TEM of Arabidopsis root cross-sections Roots were fixed with 2% glutaraldehyde in cacodylate buffer (75 mM, pH 7. 0) for 3. 5 h, postfixed with 1% osmium tetroxide at 4°C overnight. Samples were dehydrated through a series of graded acetone concentrations, 30% to 100%, and finally embedded in plastic according to Spurr  60. Ultrathin sections were obtained with a ultramicrotome (Ultracut E, Leica-Reichert-Jung, Nußloch, Germany) and stained with uranyl acetate followed by lead citrate  61. Sections were viewed with a LEO 906 E TEM (LEO, Oberkochen, Germany) equipped with the Wide-angle-2K (4Mpx. ) Dual Speed CCD Camera (TRS, Moorenweis, Germany) using the software ImageSP-Professional to acquire, visualize, analyse, and process image data.
AP-MS on CYCB3;1 Cloning of CYCB3;1 encoding the C-terminal GS rhino tag 63 fusion under control of the constitutive cauliflower tobacco mosaic virus 35S promoter and transformation of Arabidopsis cell suspension cultures (PSB-D) with direct selection in liquid medium was carried out as previously described  64. Pull downs were performed in triplicate, using in-house prepared magnetic IgG beads and 25 mg of total protein extract per pull down as described  64. On-bead digested samples were analyzed on a Q Exactive (ThermoFisher Scientific) and co-purified proteins were identified with Mascot (Matrix Science) using standard procedures  64. After identification, the protein list was filtered versus a large dataset of similar experiments with non-related baits using calculated average Normalized Spectral Abundance Factors (NSAFs)  64.
Sample preparation and LC-MS/MS data acquisition for the identification of EDE1 phosphosites The protein mixtures were reduced with dithiothreitol, alkylated with chloroacetamide, and digested with trypsin. These digested samples were desalted using StageTips with C18 Empore disk membranes (3 M)  68  , dried in a vacuum evaporator, and dissolved in 2% ACN, 0. 1% TFA. Samples were analyzed using an EASY-nLC 1200 (Thermo Fisher) coupled to a Q Exactive Plus mass spectrometer (Thermo Fisher). For initial assessment of phospho sites, peptides (1:10 dilution) were separated on 16 cm frit-less silica emitters (New Objective, 75 μm inner diameter), packed in-house with reversed-phase ReproSil-Pur C18 AQ 1. 9 μm resin (Dr. Maisch). Peptides were loaded on the column and eluted for 50 min using a segmented linear gradient of 5% MS/MS spectra were acquired with a target value of 5x10 5 ions at a resolution of 17,500 FWHM, a maximum injection time of 120 ms and a fixed first mass of m/z 100.
MS data analysis and PRM method development Raw data from DDA acquisition were processed using MaxQuant software (version 1. 5. 7. 4, http://www. maxquant. org/)  69. MS/MS spectra were searched by the Andromeda search engine against a database containing the respective proteins used for the in vitro reaction. Trypsin specificity was required and a maximum of two missed cleavages allowed. Minimal peptide length was set to seven amino acids. Carbamidomethylation of cysteine residues was set as fixed, phosphorylation of serine, threonine and tyrosine, oxidation of methionine and protein N-terminal acetylation as variable modifications. The match between runs option was disabled. Peptide-spectrum-matches and proteins were retained if they were below a false discovery rate of 1% in both cases.
Raw data from the DDA acquisition were analyzed on MS1 level using Skyline (https://skyline. ms)  70  and a database containing the respective proteins used for the in vitro reaction. Trypsin specificity was required and a maximum of two missed cleavages allowed. Minimal peptide length was set to seven maximum length to 25 amino acids. Carbamidomethylation of cysteine, phosphorylation of serine, threonine and tyrosine, oxidation of methionine and protein N-terminal acetylation were set as modifications. Results were filtered for precursor charges of 2, 3 and 4. For each phosphorylated precursor ion a respective non-phosphorylated precursor ion was targeted as a control, furthermore several precursor ions from the backbone of EDE1 were chosen as controls between the samples. In total 17 precursors were chosen to be targeted with a PRM approach.
FRAP assay For the bleaching of GFP-EDE1, sections of the spindles were bleached with the 405 and 488 lasers both at 100% after 5 frames of imaging and with a scan speed of 7 and 5 iterations. Images were acquired every 0. 5 s with a pixel size of 120 nm. For the analysis of the images, the Stowers Plugins Collection was used (https://research. stowers. org/imagejplugins). The data processing and analysis was identified protein; pep_query: peptide query number; pep_rank: rank of the peptide match, 1 to 10, where 1 is the best match; pep_isbold: peptide is in bold red (Red and bold typefaces are used to highlight the most logical assignment of peptides to proteins. The first time a peptide match to a query appears in the report, it is shown in bold face. Whenever the top-ranking peptide match appears, it is shown in red.
Acknowledgments We thank  Dr. Roland Thünauer  for technical support at the  Advanced Light and Fluorescence Microscopy facility  at the  CSSB (DESY, Hamburg) ,  Anne Harzen  at the  MPI for Plant Breeding Research  for LC-MS/MS analysis in the identification of EDE1 phosphosites and the  VIB Proteomics Core  for the LC-MS/MS analysis of the AP-MS samples. We thank the  Cambridge Research Computing services , specifically for HPC used for this study. This work was supported by the  HFSP  grant  RGP0023/2018  to A. S. and D. B. ; F. N. , H. S. and C. J. were supported by the  Gatsby Charitable Foundation  (Grant  PTAG-024 ) and the  European Research Council  ( ERC Synergy Grant , project  951430 ).
, 2022; Gladson et al. , 2022; Yang et al. , 2022; Jung et al. , 2021; Hough et al. , 90 2021). In this paper, we benefit from the high-spatial resolution MAIAC long-term data (from 2000 to 2021) to investigate AOD over Europe. This work is part of the ACROSS (Atmospheric ChemistRy Of the Suburban foreSt, https://across. cnrs. fr/) project, whose objective is to deepen the current physical-chemical knowledge of the interaction between anthropogenic emissions on the Paris area and its surrounding environment, through an intensive field campaign which took place in the 95 summer 2022  (Cantrell and Michoud, 2022). Within the ACROSS context, this study wants to achieve three different objectives:  Investigate the urban local vs regional scale aerosol optical depth variability starting from a broader context over the European domain (20°W-40°E,30-60°N) up to the urban local scale (3x3 km 2 ) around major urban agglomerations in Europe; 100  explore the long-term trends at the urban local (3x3 km 2 ), regional (100x100 km 2 ) and continental scales (20°W-40°E,30-60°N);  contextualize the results for the Paris agglomeration with respect to other European cities.
MAIAC dataset extraction and analysis 110 The daily MCD19A2 product  (Lyapustin and Wang, 2018)  providing the AOD at 470 nm and 550 nm has been used over the period February 2000-August 2021. All the observations are delivered in the HDF4 format and stored at 1 km resolution in sinusoidal grid mapping. The product, distributed on a daily basis, contains the collection of each MODIS Aqua and Terra satellites overpasses, whose number varies according to the latitude. In this product, in order to merge the satellite data to perform the climatological averages at the European scale (20°W-40°E,30-60°N), the daily average of each tile has been number of available data, it guarantees the quality of the retrieval which is an important aspect to perform high resolution studies over urban areas. Starting from the merged MAIAC data, the following treatment is applied:   1 ): 3x3 km 2 (9 km 2 area) for the local scale and 100x100 km 2 (10000 km 2 ) for the regional scale.
Conversely, negative deviations can be linked to the presence of a non-homogenous spatial aerosol distribution at the regional scale, as well as to a possible local sink of pollution. The former may result in a stronger regional background signal related to different aerosol sources surrounding the city which may conceal the urban local signal and reduce the 140 pollution gradients. Trend assessment on AOD has been conducted over annual averages of daily AOD data using the Original Mann-Kendall test  (Hussain and Mahmud, 2019). Annual AOD averages are performed if at least 50 AOD daily data are available in the year, and trend evaluations are performed if at least 5 years data are available in the dataset. The output of the Mann-Kendall test provides the significance of the test (p-value) and the Theil-Sen slope  (Theil, 1992; Sen, 1968).
, 2020; Schutgens, 2020; Kinne et al. , 2013). In order to improve the meaningfulness against the AERONET observations, the MAIAC AOD have been additionally extracted by taking an average area of 0. 06°x 0. 06° over the AERONET site, corresponding to 165 ~7x7 km 2. Indeed  (Falah et al. , 2021)  show that MAIAC -AERONET comparisons given similar results for boxes between 1x1 km2 and 9x9 km2. Furthermore, AERONET AOD data between ±1H of the satellite passage have been considered to compare with MAIAC. The uncertainty on the AERONET AOD is ∆𝐴𝑂𝐷 𝐴𝐸𝑅𝑂𝑁𝐸𝑇 =0. 02 linked to calibration uncertainty  (Sinyuk et al. , 2020). As the differences between MAIAC and AERONET observations are attributed entirely to MAIAC uncertainty, the derived MAIAC expected error is conservative. Different statistical indicators have been calculated to evaluate 170 the comparison between MAIAC and AERONET AOD data.
Those the Mean Bias Error (MBE), the Normalized Mean Bias (NMB), the Root Mean Square Error (RMSE), and the fraction of data within a factor of two (FAC2), as defined below (N is the number of data points): 𝑀𝐵𝐸 = 1 𝑁 (𝐴𝑂𝐷 𝑀𝐴𝐼𝐴𝐶 -𝐴𝑂𝐷 𝐴𝐸𝑅𝑂𝑁𝐸𝑇 ) (5) 𝑁𝑀𝐵 = ∑(𝐴𝑂𝐷 𝑀𝐴𝐼𝐴𝐶 -𝐴𝑂𝐷 𝐴𝐸𝑅𝑂𝑁𝐸𝑇 ) ∑ 𝐴𝑂𝐷 𝑀𝐴𝐼𝐴𝐶 (6) 175 𝑅𝑀𝑆𝐸 = √ ∑(𝐴𝑂𝐷 𝑀𝐴𝐼𝐴𝐶 -𝐴𝑂𝐷 𝐴𝐸𝑅𝑂𝑁𝐸𝑇 ) 2 𝑁 (7) 𝐹𝐴𝐶2 (%) = 𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑑𝑎𝑡𝑎 𝑠𝑎𝑡𝑖𝑠𝑓𝑦𝑖𝑛𝑔 0. 5 ≤ 𝐴𝑂𝐷 𝑀𝐴𝐼𝐴𝐶 𝐴𝑂𝐷 𝐴𝐸𝑅𝑂𝑁𝐸𝑇 ≤ 2 (8) The correlation between 𝐴𝑂𝐷 𝑀𝐴𝐼𝐴𝐶 and 𝐴𝑂𝐷 𝐴𝐸𝑅𝑂𝑁𝐸𝑇 has been evaluated through the Pearson correlation coefficient R. The slope and the intercept of the regression line have been calculated taking into account the uncertainty in both coordinates using the York regression  (York et al. , 2004). The comparison between MAIAC and AERONET AOD at 550 nm for all available 180 European AERONET measurements from 2000 to 2021 is shown in Figure  2.
The overall validation performed considering the entire dataset (panel a) shows a slight underestimation of the AOD from MAIAC with respect to AERONET, with a MBE (-0. 02) and a RMSE (0. 06) values similar to those retrieved in previous validation studies  (Chen et al. , 2020; Lyapustin et al. , 2018; Martins et al. , 2017). The probability density function (PDF) of the MAIAC-AERONET absolute differences (panel b in Figure  2 ), shows a mean value and a sigma of -0. 02 and 0. 06, respectively. 77% of the AOD retrievals fall in an Since dependency of EE on aerosol type and size has been evidenced by  (Falah et al. , 2021)  a further detailed validation 190 depending on the Angstrom Exponent (AE) between 440 nm and 870 nm has been performed and presented in Figure  2  (panels c, d, e). The AE, combined with AOD, is an indicator of the particle type and size.
215 The aerosol optical depth variability at European scale is shown in Figure  3 , reporting seasonal averages, and in Figure  S1  in the Supplementary Information reporting the monthly averages of the AOD at 550 nm. The summer (JJA) season shows the highest AOD values ranging between 0. 12-0. 22 in the 30°N-60°N band, whereas DJF shows the lowest AOD values ranging between 0. 06-0. 09. These findings with the MAIAC dataset are broadly in line with a previous analysis of MODIS and MISR data  (Gupta et al. , 2023 (Gupta et al. , , 2022;; Filonchyk et al. , 2020a; Mehta et al. , 2016). A North-South AOD gradient over Europe has been also found in.
City scale analysis The 1 km resolution MAIAC AOD data are used to explore AOD levels over cities and evaluating and quantifying the extra AOD of cities with respect to their surrounding areas. Figure  4  shows the distribution and the heterogeneity of the aerosol optical depth over the different sites. European cities are ordered by increasing 50th percentile values of the local scale AOD 280 from left to right. Table  1  gives the coordinates of city centers. An example of AOD time series for some of the cities is shown in Figure  S3. In line with Section 3. 2 ). For more southerly cities like Milan and Athens, this fraction is 14. 7% and 10. 6% respectively. Looking more 290 closely to the timing of these occurrences, 18% and 10% of these "high pollution" cases occurred before and after 2010 respectively for Milan, and 14% and 8% for Athens.
For instance,  (Pirhadi et al. , 2020)  finds that due to its semi-volatile character, about 50% of ambient PM2. 5 aerosol evaporated when heated up in a 305 thermo-denuder from ambient temperature (~13°C in winter, 23°C in summer and up to 50°C). The urban heat island effect depends on the size and additional heat production within an urban area. It is restricted to light wind meteorological conditions and it is more pronounced during night, while MAIAC observations are made during daytime. For these reasons, we consider that evaporation of semi-volatile aerosol under higher urban temperatures could only play a limited effect in our dataset. Since the observed negative LTRR values were in general very small (in the order of some %) an alternative explanation to 310 negative LTRR values is a possible inhomogeneity in AOD within the rather large (100 x 100 km 2 ) regional domain.
Acknowledgements 465 This work has been supported by the  ACROSS  and the  RI-URBANS  projects. The  ACROSS  project has received funding from the  French National Research Agency (ANR)  under the investments programme integrated into France 2030, with the reference  ANR-17-MPGA-0002 , and it was supported by the  French National program LEFE  ( Les Enveloppes Fluides et l'Environnement ). The  RI-URBANS  project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  101036245. We thank all the  AERONET PIs  and their staff for establishing 470 and maintaining all the sites used in this investigation. Useful discussions with  M. Mallet ,  Y. Derimian , and  J. C. Raut  are gratefully acknowledged. We thank  C. Cantrell  and  V.