dataesr/scientific-paragraphs-categorization huggingface dataset:
6. 4, 53 using gene annotations from GENCODE (release 32 for the human genome and M23 for the mouse genome). Differential expression analyses were performed with the R package DESeq2 version 1. 30. 0. The model included the donor and the treatment (unstimulated, IL-2v polypeptide, obinutuzumab, trispecific aNKp46/Fc/aCD20, combination of trispecific aNKp46/Fc/ aCD20 + IL-2v polypeptide and tetraspecific ANKET). Log 2 fold-changes were shrunk with the lfcShrink function implemented in DE-Seq2, by the ''apeglm'' method. Gene set enrichment analysis (GSEA) was performed with the R package fgsea, version 1. 16. 0 (fgseaMultilevel function). ORA (overrepresentation analysis) was used to identify the pathways overrepresented in the lists of genes. These enrichment analyses were performed on the Hallmark, Reactome, BioCarta, PID and WikiPathways gene sets (msigdb version 7.
ACKNOWLEDGMENTS The E. V. laboratory at  CIML  and  Assistance-Publique des Hôpitaux de Marseille  is supported by funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( TILC , grant agreement no. 694502  and  MInfla-TILC , grant agreement no. 875102 -MInfla-Tilc ), the  Agence Nationale de la Recherche  including the  PIONEER  Project ( ANR-17-RHUS-0007 ),  MSDAvenir, Innate Pharma , and institutional grants awarded to the  CIML (INSERM ,  CNRS , and  Aix-Marseille University)  and  Marseille Immunopole.
Data and code availability d All sequencing data supporting the findings of this study have been deposited into the NCBI -Gene Expression Omnibus database (https://www. ncbi. nlm. nih. gov/geo/). The accession number (GEO: GSE214378) is listed in the key resources table. d This paper does not report original code. Software packages used in the analysis are also presented in the key resources table. d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Continued REAGENT or RESOURCE SOURCE IDENTIFIER Deposited data Raw data files for RNAseq This work GEO accession number: GSE214378 (https://www. ncbi. nlm. nih. gov/geo/) Software and algorithms FlowJo v. 10. 5. 2 BD Biosciences https://www. flowjo. com/solutions/flowjo/.
Fluorescence recovery after photobleaching GEM2 knock-in cells stably expressing Mito-EGFP were stained with 100 nM Halo-TMR for 30 min after the treatment with 2 µg/ml doxycycline for 1 day. Photobleaching was carried out on a Zeiss LSM780 using a Plan-Apochromat 63x/1,4 Oil DIC M27 oil-immersion objective. Bleaching was performed in a 4 µm circular region after initial 10 frames using a laser intensity 160-fold higher than the laser intensity used for image acquisition. Images were acquired every 100 ms for the course of the experiment. Intensities were normalised according to a background region outside the cell as described by Halavatyi and Terjung  29. A single exponential recovery curve was fitted to the mean of single-normalised intensities over time using FRAPAnalyser (https://github.
Cells were kept in PBS until imaging. Images were acquired on a Zeiss LSM980 confocal microscope, equipped with Airyscan detector and Plan-Apochromat 63x/1. 4 Oil DIC M27 objective. For MitoTrap, Ki-67 and Nup96, single confocal z-planes were acquired and analysed. For seipin, z-stacks covering the whole cell were acquired and analysed. Images were processed with identical settings in each dataset using Zen Blue software (Zeiss). Cell boundaries were manually determined in Fiji. GEMs and target proteins (MitoTrap, Ki-67, Nup96 or seipin) were segmented using ilastik  31  and the fraction of overlapping areas was analysed using CellProfiler  32.
Cryo-ET image acquisition Cryo-TEM montages and tilt series were collected on a Titan Krios G3 (Thermo Fisher Scientific) equipped with a Gatan K2 Summit detector and Quantum energy filter, or a Gatan K3 detector and BioQuantum energy filter, using SerialEM 39. Grids were loaded such that the lamella pre-tilt axis aligns with the microscope stage tilt axis. Images were acquired at a pixel size of 3. 370 or 3. 425 Å/px at 1. 5-4 µm defocus, with an electron dose of 2. 0-2. 5 e -/Å 2 per image fractionated over 8-10 frames. A dose-symmetric tilt scheme  40  was used with 2° increments starting from the lamella pre-tilt (±13°) and an effective tilt range of +56° to -56°. Data were collected with a 70 µm objective aperture or Volta phase plate inserted, and 20 eV slit width.
The initial training set contained 161 particles from 39 tomograms, whereas the final training set contained 1284 particles from 71 tomograms. The number of grids and tomograms contributing to this final dataset was as follows: Mito, 6 grids, 12 lamellae, 18 tomograms; Ki-67, 3 grids, 3 lamellae, 9 tomograms; Nup96, 2 grids, 6 lamellae, 20 tomograms; seipin, 5 grids, 9 lamellae, 24 tomograms. Subtomograms at 6. 85 Å/px with a box size of 128 px and 3D CTF models were reconstructed in WARP. Averaging was performed in RELION  45  with 1284 particles, I1 symmetry, using a 60-Å-low-pass-filtered map of the encapsulin scaffold (PDB 6X8M) as reference. Membranes were segmented by tensor voting with TomoSegMemTV 46 and manual cleaning in Amira (Thermo Fisher Scientific). ER-LD contact sites were defined manually in Amira.
Data and code availability The tomogram of GEM2-labelled Mito-GFP and accompanying mitochondria and GEM annotations presented in Fig. 2a , and the subtomogram average of GEM2 presented in Fig. 2b  will be deposited into the EMDB. Raw data corresponding to the full Mito-GFP cryo-ET dataset analysed in Fig. 2c  will deposited into EMPIAR. The DeePiCt CNN model for automated GEM detection and Python script for GEM-target distance analyses will be available via GitHub (https://github. com/hermankhfung). CRISPR knock-in donor plasmids generated in this study will be deposited into Addgene. Cell lines generated in this study are available upon request.
Acknowledgements This work was supported by the  Deutsche Forschungsgemeinschaft (DFG)  - SPP2191  Project ( 419120233  to H. K. H. F. , 402723784 to S. C. -H. and J. M. ), the  European Molecular Biology Laboratory  ( EMBL) Nuclear Architecture Seed Grant  (to H. K. H. F. and Y. H. ), the  EMBL   Interdisciplinary Postdoctoral Program under Marie Skłodowska-Curie Actions COFUND  ( 664726  to H. K. H. F. and Y. H. ), the  Human Frontier Science Program  ( CDA00045/2019  to S. C. -H. ),  Marie Skłodowska-Curie Actions  ( 101028297  to V. T. S. ), the  Biomedicum Helsinki Foundation  (to V. T. S), the  Orion Foundation  (to V. T. S), and the  Boehringer Ingelheim Fonds PhD fellowship  (to A. B. ). We thank the laboratories of  Tobias Walther  and  Robert Farese  for kindly providing endogenous seipin-sfGFP knock-in and seipin knock-out cell lines, the laboratory of  Daniel Gerlich  for providing endogenous EGFP-Ki-67 knock-in and Ki-67 knock-out cell lines.
Data Analysis Cefiderocol time-courses were fit to a two-compartment open model with first-order elimination. The following compartmental parameters were then derived: CL, Q, V1 and V2, which stand for the elimination and inter-compartmental clearances, central and peripheral volumes of distribution, respectively. The nonlinear mixed effect modelling program Monolix version 2020R1 (Lixoft, Antony, France) (http://lixoft. com (accessed on 1 January 2022)) was used for the model development. The between-subject, BSV or ω and residual variabilities (square roots of the variances ω 2 and σ 2 ) were ascribed to an exponential distribution. The influence of demographic and clinical characteristics that could affect cefiderocol pharmacokinetics, i. e. , sex, total bodyweight (BW), FFM, age and renal function (CKD equation) were investigated.
Progesterone Quantification The progesterone level was determined in peripheral blood plasma samples each week by ELISA (IBL international; Germany) according to the manufacturer's instructions (Figure  1B ). Progesterone groups were determined as follows: high progesterone (>1. 4 ng/mL), low progesterone (<1. 4ng/mL), and menstruation (bleeding and/or low progesterone level after a peak of progesterone). We estimated the displacement between the menstrual cycle of the animals (through progesterone levels) using the popular methodology for time series alignment: crosscorrelation maximum. Computations were carried out using the Python package SciPy  (24). The optimal lag is derived as the one that maximizes the cross-correlation, which is a similarity measure between time-series as a function of the lag.
Neutrophil Phenotyping Neutrophil populations were analyzed in whole blood and cervicovaginal cells. Cervicovaginal cells were filtered using a 35 µm filter (Corning Falcon; USA). Then, cervicovaginal cells and whole blood were incubated with the antibodies listed in Table  2 , washed, and fixed with FACS lysing buffer (BD, Biosciences) or BD cell Fix solution (BD, Biosciences). A 14-color panel containing neutrophil maturation and activation surface markers was used. Phenotyping was performed on a Fortessa instrument (BD, Biosciences) using DIVA (BD) and FlowJo (Tristar, USA) software. The gating strategy for cervicovaginal cytobrushes is described in Supplementary Figure  1.
DNA Extraction and 16S rRNA Gene Sequencing DNA from vaginal fluids was extracted using the PowerFecal DNA Pro isolation kit (Qiagen; Germany) following the and swabs, as well as cervicovaginal cells, were collected once a week for three months. Samples were not collected during weeks 5 and 6. Image created in BioRender. com. (B) Progesterone concentrations were quantified in plasma once a week for all individuals. The graphical representation of progesterone concentration including all animals was obtained using interpolated and aligned data (left). On the contrary, the graphical representation of progesterone concentration for each individual was obtained using the raw data (right). Red arrows represent menstruation. manufacturer's instructions. DNA was quantified on a Qubit 4 fluorimeter using the high sensitivity DNA kit (Life Technologies; USA).
The PCR reaction was carried out using a T100 thermal cycler with an initial denaturation step (94°C for 10 min), 12 cycles of amplification (94°C for 1 min, 65°C for 1 min and 72°C for 1 min), and a final elongation step at 72°C for 10 min. All libraries were pooled using equal amounts to generate an equivalent number of raw reads for each library. The pool, at a final concentration of 5 to 20 nM, was used for sequencing. The PhiX Control v3 (Illumina, USA) was added to the pool at 15% of the final concentration, as described in the Illumina procedure. Then, 600 ml of this pool and PhiX mixture was loaded onto an Illumina MiSeq cartridge according to the manufacturer's instructions using the MiSeq Reagent Kit v3. FastQ files were generated at the end of the run (MiSeq Reporter software, Illumina, USA) to perform the quality control.
Sequencing Data Processing and Taxonomic Assignation Illumina sequences were processed using the FROGS pipeline (Find Rapidly OTU with Galaxy Solution)  (27). Bacterial 16S rRNA paired-end reads were merged with a 0. 1 maximum rate of mismatch in the overlap region using Vsearch  (28). Each of the samples was from a unique timepoint. Thus, after dereplication, the clustering step was run with an aggregation distance equal to 1 (maximum number of differences between each of our sequences) and denoising was not required. Chimeras were also removed using Vsearch. More than 93% of the total sequence abundance was retained. Sequences were then filtered to keep at least 0. 0005% of all sequences and the phiX databank was equally applied to eliminate Illumina contaminants. Finally, taxonomic affiliation was performed using the SILVA 138 pintail 100 database.
Statistical Analysis Heatmaps representing fold changes of cytokine concentrations in cervicovaginal fluids or peripheral blood plasma were obtained using Tableau software (Seattle, USA). Ordinary oneway ANOVA was used to compare fold changes of cytokine concentrations to the mean total fold change as a reference. GraphPad prism software version 9 for windows (GraphPad Software, La Jolla California USA, (http://www. graphpad. com) was used for graphical representation of the vaginal microbiota composition (pie-charts), cytokine concentrations, percentage of neutrophil subpopulations, and bacterial abundance according to progesterone level. Significant differences between groups were confirmed using either a paired T-test or Kruskal-Wallis test with p values adjusted using Dunn's test. The MetagenomeSeq fitZIG algorithm (  29 ) was used to determine differentially abundant bacteria according to the menstrual phase.
Significant differentially abundant cytokines, neutrophils, and microbiome data were merged into one global heatmap using R (version 4. 1. 0) with the pheatmap package (version 1. 0. 12). Data were normalized and the fold change calculated using the mean value for the variable in all samples as the reference. A log10 transformation was applied directly to the fold change to manage extreme values. Samples were clustered using a hierarchical clustering approach available in the pheatmap function. Annotation colors were added to visually assess whether the samples clustered by animal or by progesterone level. Finally, absent measurements in the data were flagged as non-determined (ND) and values with an abundance of zero were colored in grey.
043% ± 0. 00) (Supplementary Figure  7  and Figures 6A, C ). We represented the top nine genera or families for all or each animal according to the hormonal phase to identify changes in bacterial abundance (Figure  7  and Supplementary Figure  8 ). Bacterial abundance and composition varied according to the hormonal group for all animals. We assessed whether bacterial diversity was affected by the hormonal phase by calculating the Shannon diversity index according to the high-or lowprogesterone or menstruation timepoints. We observed higher bacterial diversity (p value: 0. 0065) during menstruation than during the high-progesterone phase (Figure  8A ). We then performed a statistical analysis of the relative abundance of all families and genera adapted to the high throughput sequencing data for all animals to identify bacterial families and genera that were differentially abundant between progesterone groups (Figures  8B, C ).
ACKNOWLEDGMENTS This work benefited from the facilities and expertise of  @BRIDGe  ( GABI, INRA, AgroParisTech, Paris-Saclay University ). The authors would like to thank all members of the  ASW (Raphaël Ho Tsong Fang, Nina Dhooge)  and  L2I  ( Nathalie Bosquet ,  Laetitia Bossevot ,  Julie Morin ) and teams of the IDMIT infrastructure, as well as the members of the  Animalliance group  ( Thierry Prot ,  Marjorie Benfissa ). The authors would also like to thank the bacteriology-virology service of the  Lariboisière Hospital (Paris) , including  Dr HervéJacquier , for their help in setting up the Nugent scoring for the vaginal samples from the cynomolgus macaques.
FUNDING The program was funded by the  Infectious Disease Models and Innovative Therapies (IDMIT)  research infrastructure supported by the " Programme Investissements d'Avenir ", managed by the  ANR  under reference  ANR-11-INBS-0008 , as well as Sidaction "financement jeune chercheur" under reference " 2019-2-FJC-1234 ". The  Non-Human Primate study  is part of the  TracVac  project, which received financial support from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 733373.
| Information sources We conducted a systematic search of the literature, in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses  (Moher et al. , 2009). The search covered a period up to September 2019 in the following databases: arXiv (19), Cochrane Library (48), Embase (925), IEEE Xplore (425),  PsycINFO (643), PubPsych (190) , PubMed (684), Science Direct (3759), SpringerLink (4302),  Taylor & Francis Online (783) , Web of Science (1220). A combination of the following keywords was used: "EEG or electroencephalogra*" and "cognitive load"; "EEG or electroencephalogra*" and "cognitive workload"; "EEG or electroencephalogra*" and "mental load", "EEG or electroencephalogra*" and "mental workload". Moreover, we manually performed a search in the major reviews of the field  (Borghini et al.
| Data collection When reported, we extracted the following information from each study: (1) sample size, (2) mean age (and standard deviation) and gender of participants, (3) research domain, (4) study design (within-or between-participants), (5) frequency band(s), (  6 ) electrode position, (7) number of tasks and method used to increase the CWL, (8) time on task, (9) method used to estimate spectral power, and (10) statistical data used to calculate effect sizes. Datafiles and the R script for the meta-analysis can be found on the Open Science Framework (OSF) through the following link: https://osf. io/xrb4z/. F I G U R E 1 PRISMA flow diagram of the systematic search.
| Summary measures Statistical analyses were conducted in RStudio (RStudio Team, 2020, version 1. 3. 1093) using R (R  Core Team, 2013, version 4. 0. 1). We used the esc package in order to compute the effect sizes  (Lüdecke, 2018) , and the meta  (Schwarzer, 2007) , metafor  (Viechtbauer, 2010) , and dmetar  (Harrer et al. , 2019)  packages to conduct the meta-analyses and meta-regression. We computed a Hedges's g statistic for the standardized mean difference in bandwidth power between high and low workload conditions. If the experimental comparison involved more than two load levels, the standardized mean difference was between the harder and the easier condition. Since most studies evaluated this difference in the same individual (within-subject design), we calculated repeated-measures effect sizes.
| Study selection After duplicates had been removed, 5 716 unique records were identified in searches through the database and reference list. 4 916 records were then excluded from the preliminary screening of titles. Among the remaining 800 records, 570 were excluded after screening of abstracts because they did not manipulate CWL (69), they did not include any EEG measure with spectral power analysis (74), they were a book chapter (89), conference paper (205), review article (39), dissertation (7), inaccessible (4), technical report or article using classification algorithms (67), not in English (10) or focused on a clinical population (6). Two hundred and thirty reports were retrieved for detailed evaluation of the full-text and a total of 24 records met the inclusion criteria and were included in the quantitative review (see Figure  1 ).
, 2017; Goldberg and Bernhofer, 2001). These observation-based provide valuable information to evaluate ESMs on how well they capture the controls of LE. Using this estimate, De  Kauwe et al. (2013)  found that one of the principal reasons for disagreement among simulated transpiration responses to elevated CO 2 is the differences in the degree of coupling between vegetation and the atmosphere. The Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model (LSM) is one of the widely used models for simulating carbon, energy and water budget of terrestrial ecosystems (e. g. ,  Zhang et al. , 2021; Schrapffer et al. , 2020). ORCHIDEE and the ESM Institute Pierre-Simon Laplace climate model (IPSLCM), which has ORCHIDEE as the land surface module, have participated in various model intercomparison projects including TRENDY, Coupled Model Intercomparison Project (CMIP), etc.
In spite of its wide usage, the LE of the ORCHIDEE LSM remains simply calibrated and evaluated against the total evapotranspiration observations  (Bastrikov et al. , 2018) , without considering the detailed processes. A recent study showed that the ORCHIDEE version used in CMIP6 still has biases in LE, especially in tropical regions  (Tafasca et al. , 2020). However, it remains unclear how the biases happened and which processes need to be improved to better simulate the fluxes. To solve this problem, in this study, we used the dataset derived from eddy covariance data from 90 sites  (De Kauwe et al. , 2017)  to evaluate the vegetation-atmosphere coupling strength of the land surface model ORCHIDEE 2. 2 (v7266). We tested whether the calibration of the stomatal response to atmospheric dryness, or using observed canopy height, can improve the simulation of coupling strength.
ORCHIDEE model We use the ORCHIDEE 2. 2 (v7266) land surface model in this study. This model version is the latest version participating in the CMIP6 project under coupled configuration to the atmospheric circulation model in the IPSL-CM6A-LR ESM  (Boucher et al. , 2020). The ORCHIDEE model consists of three interactive sub-modules  (Krinner et al. , 2005). The SECHIBA module parameterizes the land surface energy and water balance  (Ducoudré et al. , 1993). The STOM-ATE module deals with phenology  (Botta et al. , 2000)  and carbon fluxes of terrestrial ecosystems  (Viovy, 1996). The LPJ dynamic vegetation module simulates the dynamics of vegetation  (Sitch et al. , 2003). In this study, the dynamic vegetation module is turned off because the vegetation types are prescribed at each site. ORCHIDEE simulates LE by considering plant transpiration, bare soil evaporation, sublimation, floodplain evaporation, and evaporation from canopy water interception.
Since this study focuses on the vegetation-atmosphere coupling strength for transpiration and because the data to evaluate this model have been filtered to represent the transpiration  (De Kauwe et al. , 2017) , we only introduce the parameterization of conductance relating to transpiration in ORCHIDEE here. The stomatal conductance (gs, mol m -2 s -1 bar -1 ) is calculated in the photosynthesis module which couples the leaflevel photosynthesis and stomatal conductance based on  (Yin and Struik, 2009) : gs = g0 + A + R d C i -C * i f vpd , (1) where g0 is the stomatal conductance when the irradiance is zero (mol m -2 s -1 bar -1 ); A is the rate of CO 2 assimilation (µmol m -2 s -1 ); R d is the dark respiration (µmol m -2 s -1 ); C i is the intercellular CO 2 partial pressure (µbar); C * i is the C i -based CO 2 compensation point (µbar) in the absence of R d ; and f vpd is the function for the effect of vapor pressure deficit (VPD, kPa) on stomatal conductance, calculated as f vpd = 1 1 a 1 -b 1 VPD -1 , (2) where a 1 and b 1 are empirical parameters depending on vegetation type (Fig.
FLUXNET data and empirical calculation of The empirical reference is derived from the FLUXNET 2015 dataset  (Pastorello et al. , 2020). This dataset collects eddy covariance measurements of heat and water fluxes, as well as the corresponding meteorological variables above the vegetation canopy in sites over the world and across different plant functional types (PFTs). The detailed information of the flux sites used can be found in Table  S1  in the Supplement. The calculation of was firstly introduced by  Jarvis and McNaughton (1986) , using the formulation: = 1 + 1 + + Ga Gs , (9) where = s/γ ; s is the slope of the saturation vapor pressure curve with air temperature (Pa K -1 ); γ is the psychrometric constant (Pa K -1 ). It should be noted that the conductance (Ga, Gs) used for calculation depends on the scale of interest.