dataesr/scientific-paragraphs-categorization huggingface dataset:
S5  and S6 †). The binder models were subjected to 21 MD step simulations with seven cycles of three MD simulations performed in NVT (T = 600 K), NVT (T = 300 K) and NPT (T = 300 K) ensembles. The value of the pressure in the NPT simulations was increased from 1 bar to 50 kbar in the rst three cycles out of the seven cycles and then gradually decreased until it reached ambient pressure. Temperature and pressure were regulated by the Nosé-Hoover thermostat and barostat,  54, 55  respectively. The coupling constant of both the thermostat and barostat was 0. 1 ps. The time step of the simulation was chosen to be 1 fs. These MD simulations were performed using the LAMMPS simulation soware. (DFT) level, as implemented in the Vienna Ab initio Simulation Package (VASP, 5. 4. 4 version). 57  The projector augmented wave (PAW) method was utilized to describe the electron-ion interaction.
The atomic partial charges of each slab surface were calculated using the density derived electrostatic and chemical (DDEC6) method as implemented in the CHARGEMOL module. 66, 67  The x-and y-dimensions of the resulting slab surface model were then expanded ve times for further processing the construction of the MOF/binder composites at the force eld level. The universal force-eld (UFF) parameters  68  were selected to dene the bonded and non-bonded terms of the MOF slab model. The force-eld parameters of the MOF can be found in the ESI (Table  S10  †). Notably, a few equilibrium bond length parameters (Zn-O and Zn-N) of the UFF were tuned to ensure an excellent correspondence between the mean value of the bond distances, bending angles, and dihedral angles of the MD-equilibrated MOF slab structure and the corresponding geometric features obtained for the DFT optimized structure (Table  S11  †).
Gas adsorption and diffusion simulations Grand Canonical Monte Carlo (GCMC) simulations were performed to assess the single component CO 2 , CH 4 , and N 2 adsorption isotherms and the binary mixture of CO 2 : N 2 (molar concentration ratio of 15 : 85) of CALF-20 and its binder-based composites at 300 K in the pressure range of 0. 001 to 5 bar. The GCMC simulations were performed with Complex Adsorption and Diffusion Simulation Suite (CADSS) code 74 with 10 7 steps of equilibration followed by 10 7 steps of production run for each point in the adsorption isotherm. CO 2 was modeled by a 3-site linear rigid LJ model,  75  CH 4 described by a single-site LJ model,  49  and N 2 treated as a 3-site linear rigid LJ model  76  two sites for the nitrogen atoms and a third ctitious site for their center of mass.
Data Availability Statement The EMFISIS data and orbit parameters (L, MLT, etc. ) of the Van Allen Probes are publicly available at the Web https://emfisis. physics. uiowa. edu/. The IAP data and orbit parameters (L, MLT, etc. ) of the DEMETER satellite are available at the Web site http://demeter. cnrs-orleans. fr/. AE index is publicly available from the OMNI database at the CDAWeb http://cdaweb. gsfc. nasa. gov/sp_phys.
, 2006; Chalmandrier et al. , 2017; Münkemüller et al. , 2012). For example, biotic filters may be overlooked if distances between studied individuals are too large  Blanchet et al. , 2020. Despite the awareness that scale can be the key in revealing community assembly rules, attempts to develop a spatially explicit approach are so far limited to either ad hoc manipulations of the dataset and/or species pool  (Chalmandrier et al. , 2017; de Bello et al. , 2013) , comparisons of only two opposing scales  (Carboni et al. , 2013; Cavender-Bares et al. , 2006)  or case-specific comparisons that do not provide a stringent spatially explicit framework  Trisos et al. , 2014; Xu et al. , 2021. If relevant scales for environmental and biotic filters are unknown, we require a continuous spatially explicit sampling protocol and trait-based diversity analysis to disentangle assembly processes.
Regional species pool We extended our regional species pool beyond the list of species in the experiment to be able to account for all species in the catchment area and thus all species available to enter the local communities Carstensen et al. , 2013. The regional species pool (787 species) included species from (i) our experimental sites, (ii) botanical surveys from the long-term monitoring program ORCHAMP along the elevational gradient of the transplant experiment (i. e. , from 1920 to 2700 m at five different sites, https://orcha mp. osug. fr/sites/ 5), and (iii) botanical surveys (i. e. , 747,387 observation points) from the Alpine National Botanical Conservatory (CBNA; http://www. cbn-alpin. fr/) taken within a 5-km buffer around the experimental sites. As our experimental sites are grasslands, we excluded shrubs, trees and understory herbs from the regional pool to avoid a signal of an additional habitat filtering process.
Interspecific functional trait data We considered five different traits that reflect different functional plant strategies and competitive strength within communities (Table  1 ). We also considered an integrative trait of vegetative height, specific leaf area (SLA) and leaf dry matter content (LDMC). These three traits represented the multidimensional trait space of the species pool well (Figure  S4 ). We log-transformed and then we transform the trait values so that they follow a normal distribution with the Gaussianize function for skewed heavy tails in LambertW package  (Goerg, 2022 ; Figure  S5 ). We extracted the interspecific functional trait data from our in-house trait measurements  (Brun et al. , 2022; Thuiller et al. , 2014)  available in the Androsace database (https://origi nalps.
osug. fr/) and complemented the missing data for all the traits in Table  1  from the TRY database Kattge et al. , 2020 and the Tundra Trait Team database Bjorkman, Myers-Smith, Elmendorf, Normand, Thomas, et al. , 2018. We detail the data selection and manipulation processes from these databases in Appendix S2. Species with no trait information at all were removed (3. 8% of the regional pool), remaining species formed the updated species pool. The integrative trait was only calculated for those species for which all three traits were available (619 species, 82% of the updated species pool). Leaf CN ratio and seed mass were calculated based on 525 and 476 species (69% and 63% of the updated species pool) respectively. Based on species relative abundances, the coverage of the trait data in the experimental communities was above 97% for all traits (Figure  S3 ).
Statistical analyses First, we tested for significant patterns of functional convergence and divergence of focals independently for each neighbourhood distance and for all metrics, traits and experimental treatments. We used the two-sided Wilcoxon signed-rank test (wilcoxon. test in R) to test if the focal individuals' SES values were significantly different from zero in order to control for multiple testing  Bernard-Verdier et al. , 2012. Independently for all metrics, traits and experimental treatments, we built linear mixed-effects regression models with spatial distances as explanatory variables, SES functional distances as responses and repetition plots as random effects (lme4 package in R). In functional distance calculations, neighbourhoods overlapped. We corrected for this by weighing each observation by its pinpoints' nonoverlap ratio within its plot.
We calculated the latter as follows: where Overlap is the number of overlaps of a pinpoint i with any of the neighbourhoods in repetition plot j and N is the number of pinpoints within the same repetition plot. (1) We calculated predictions with the lmerTest and the ggeffects package  (Kuznetsova et al. , 2017; Lüdecke, 2018). We used slope, intercept and the predicted value at the largest neighbourhood distance to summarize these regressions and to interpret them according to our framework (Figure  1C ). We did not rely on significance tests of the regression models as measured functional distance values were not necessarily normally distributed and independent from each other. We thus repeated the entire analyses on 100 random species compositions, using the same null model as described above.
We then compared intercepts and slopes of these "test" data with our observed data and based the significance test on this comparison (Figures S8, S9 and S10). We report deviations from random patterns as significant if values are lower or higher than 95% of random values (i. e. , two-sided null hypotheses) and as strong tendencies if they are higher than 95% of random values or lower than 95% of random values (i. e. , one-sided null hypotheses). WMDN = ∑ N i=1 w i × d i ∑ N i=1 w i (2) MDN = ∑ N i=1 d i N (3) SES = Metric observed -mean Metric Null sd Metric Null (4) 1 - Overlap ij ∑ N i=1 Overlap ij We detected the potential signal of any biotic interaction through a significant slope between spatial and functional distances. We further checked if there is a potential signal of facilitation in our system with an additional spatial aggregation analysis (Appendix S3; Figure  S6 ).
Fourth, how much we can learn from applying this framework depends first and foremost on the trait data used in the study. Here, we used species-specific measures of aboveground traits from trait databases. If finances and time allow, it could be highly valuable to include on-field trait measurements to be able to further investigate the role of species-specific phenotypic plasticity to warming in reshaping the communities under climate change  Henn et al. , 2018. Finally, we acknowledge that a massive data collection is needed for this framework. This may not be optimal in the context of global change research, especially if we would like to (i) cover large spatial scales to provide representative results, (ii) have multiple repetitions during or between years to capture transient responses relevant for society and (iii) integrate plastic responses of species traits to account for ecosystem adaptability.
B. B. , W. T. and T. M. conceived the ideas; B. B. and T. M. designed the methodology; B. B analysed the data and led the writing of the manuscript together with T. M. and W. T. ; B. B. , M. -P. C. , J. R. , M. G. and I. C. S. realized the botanical surveys; J. R. managed the database. All authors gave final approval for publication. AC K NO W L E DGE M E N T SThis research was funded by the DIPEE/FREE-Alpes federation, the French Agence Nationale de la Recherche (ANR) through the TransAlp project (ANR-20-CE02-0021) and the French research infrastructure AnaEE (ANR-11-INBS-0001AnaEE-Services). This research was conducted within the Long-Term Socio-Ecological Research (LTSER) platform Lautaret-Oisans. This study would not have been possible without the help of interns and colleagues during the field and laboratory work.
All the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad. univ-greno ble-alpes. fr), which is supported by Grenoble research communities. F U N DI NG I N FOR M AT ION DIPEE/FREE-Alpes federation; French Agence Nationale de la Recherche (ANR), Grant/Award Number: ANR-20-CE02-0021.
Long-read sequencing and data analysis Whole-genome libraries were prepared using 1 µg of HWM-DNA of > 10 Kb fragments with the Ligation Sequencing Kit SQK-LSK109 (Oxford Nanopore Technologies, ONT, Oxford, UK), following the recommended protocol. DNA fragments were first endrepaired using the Ultra II End-Repair Prep module (New England BioLabs) and then incubated with the NEBNext Quick T4 DNA ligase (New England BioLabs) and 1D adapter mix (ONT). After purification, the library was sequenced on R9. 4. 1 flowcells (FLO-MIN106D) on a Pro-methION device (ONT) with at least 30 × coverage. Bioinformatic analysis of the LRS data is summarized in Additional file 1: Figure  S1. Briefly, base calling was performed using Guppy. Raw reads were aligned to the human reference assembly (GRCh38/hg38) using the Minimap2  [38].
SV calling from the aligned BAM files was performed using CuteSV  [39] , SVIM  [40] , Sniffles, and Sniffles2  [24, 25]. Sniffles2 was executed in multisample SV calling mode by combining variant calling of 34 samples from the same sequencing batch. To improve SV calling in repetitive regions, the '-tandem-repeats' parameter was used in combination with the bed file recommended by Sniffles2 (https:// github. com/ fritz sedla zeck/ Sniffl es). Prioritization of likely pathogenic SVs was performed by filtering out SV calls by allele frequency (AF), keeping variants with an AF < 0. 2, and batch frequency, keeping unique variants within a cohort of 34 samples. SV calls were visually inspected using IGV and Ribbon  [41]  visualizers.
Funding This research was funded by grants from  European Advanced Infrastructure for Innovative Genomics (EASI-Genomics)  ( PID10291 ),  Instituto de Salud Carlos III (ISCIII)  of the  Spanish Ministry of Health  through the grants  PI17/00164  and  PI20/00851 , co-funded by  European Regional Development Fund (FEDER funds) "A way to make Europe", Center for Biomedical Network Research on Rare Diseases (CIBERER ,  06/07/0036 ),  IIS-FJD BioBank  ( PT20/00141 ),  Fundación ONCE  ( Organización Nacional de Ciegos Españoles ),  Fundación Mutua Madrileña ,  Federación Española de Enfermedades Raras (FEDER) , and the University Chair UAM-IIS-FJD of Genomic Medicine. G. N-M was supported by a fellowship from the  Comunidad de Madrid  ( PEJ-2020-AI/BMD-18610 ), A. D. was supported by a Ph. D. fellowship from  ISCIII  ( FI18/00123 ), and P.
Dye incorporation and cRNA yield were checked using Dropsense™ 96 UV/VIS droplet reader (Trinean, Belgium). Six hundred ng of Cy3-labeled cRNA were hybridized on the Agilent SurePrint G3 Mouse GE microarray slides following the manufacturer's instructions. Immediately after washing, the slides were scanned on Agilent G2505C Microarray Scanner using Agilent Scan Control A. 8. 5. 1 software and fluorescence signal extracted using Agilent Feature Extraction software v10. 10. 1. 1 with default parameters. Microarray data were analyzed using R  (34)  and Bioconductor packages (v 3. 0) 2  (35) as described in GEO accession. Hierarchical clustering was performed using Pearson's correlation coefficient as distance function and Ward as linkage method. Partial least squaresdiscriminant analysis (PLS-DA) was performed for each tissue, with group (Ctrl or stress) as a Y response, using ropls package  (36).
Transformed signals were mean-centered and divided by the standard deviation of each variable. A model was fitted using the limma lmFit function  (37). Pair-wise comparisons of biological conditions were applied using specific contrasts. Probes with Benjamini-Hochberg (BH) false discovery rate (FDR) < 0. 05 were considered to be differentially expressed between conditions. Volcano plots were constructed with the ggplot function of the R ggplot2 package. The differentially expressed gene datasets were uploaded into Ingenuity Pathway Analysis software (Qiagen IPA, content version 28,820,210) and a core analysis was performed, with the Agilent SurePrint G3 Mouse GE microarray as background. The canonical pathways with BH ps < 0. 05 only and the upstream regulators with no flag "bias, " activation z-score < -2 or > 2 and value of p of overlap < 0.
05 only were considered. Another analysis was realized using ConsensusPathDB-mouse  (38) , with the differentially expressed gene list vs. the mus musculus database. KEGG pathways with a q-value < 0. 05 were considered. For TLDA, analysis was performed with the R statistical software  (34). For each gene, we compared the expression values within each maternal group in males and females, in C3H/HeN and C57Bl/6J apart. Pair-wise comparisons of maternal groups were conducted using a permutation test, as implemented in the oneway_test function of the coin package in R. For each set of tests (i. e. , all tested genes for a given pair of maternal groups), p-values were BH adjusted for multiple testing. Differences were considered significant when p-adj < 0. 05. The TLDA data clustering was performed using the ClustVis web tool for visualizing clustering of multivariate data 3  (39).
PCR amplification was performed on the 7900HT Real-Time PCR System (Applied Biosystems) using SDS 2. 4 software with standard conditions: 2 min 50°C, 10 min 94. 5°C, 30 s 97°C (40 cycles), 1 min 59. 7°C. Threshold cycle (Ct) values were calculated with the ExpressionSuite v1. 0. 3 software (Applied Biosystems). The detection threshold was set manually for all genes and was the same for each assay in all tissues. Ct = 39 was used as the cut-off above which, expression level was set to 0. On the TLDA array, 96 genes were studied for each sample: 87 target genes and 9 reference genes (Supplementary Figure  S2 ). Target genes were chosen among the differentially expressed genes found in the microarray experiment in different categories of pathways and biological function. Six on the 9 reference genes were defined as the best reference, using the GeNorm software  (41).
Statistics All data are expressed as the means ± SEM (standard error of the mean). Statistical analyses were performed with Statistica 6. 0 (StatSoft, Tulsa, OK, United States) and visualized in Prism 9. 0 (GraphPad Software, San Diego, CA, United States). Normality and homogeneity of variances were assessed using the Shapiro-Wilk test and Levene test, respectively. Statistical outliers were detected using Grubb's test. Body weight, hormones, and progressive ratio data were analyzed using a non-parametric Mann-Whitney U test. Behavioral data were analyzed using three-way or four-way ANOVAs with repeated measures with Strain (C57Bl/6J, C3H/HeN) and MS (Control, MS) as between factors and Lever (Active, Inactive), Session (1,2,3,4,5), RR  (5, 10, 20) , Devaluation (Non-devaluated, Devaluated) or Concurrent choice (Milk, Milk+Chow) as within factors.
No differentially expressed genes (DEGs) were found in the mPFC and hypothalamus (Supplementary Figures  S2C, F ),  while 375 DEGs were found in NAc: 306 downregulated and 69 upregulated (Figure  6C  and Supplementary Table  S1 ). Therefore, the transcriptional effect is restricted to the NAc tissue. A bioinformatic analysis of functions and networks was carried on the 375 DEGs using the IPA software and the ConsensusPathDB-mouse (CPDB) website. This detected an overrepresentation of 8 canonical pathways with BH ps < 0. 05 and 18 KEGG pathways with q < 0. 05, respectively (Figure  6D ). Among these pathways, 2 are common to the 2 analyses: GABA receptor signaling/GABAergic synapse and Glutamate receptor signaling/Glutamatergic/synapse. Pathways analysis also pointed out stress related pathways (α-adrenergic signaling and CRH signaling) and addiction pathways (nicotine, morphine cocaine).
Patients and treatment Between 19 March 2021 and 4 May 2022, a total of 69 patients were enrolled and underwent leukapheresis, representing the full analysis https://doi. org/10. 1038/s41591-023-02572-5 performance status of 0 or 1 (n = 61; 98. 4%). The majority of patients (n = 52; 83. 9%) were histologically diagnosed with diffuse large B cell lymphoma (DLBCL). In total, 34 patients (54. 8%) had primary refractory disease to first-line chemoimmunotherapy. Patients were deemed ineligible for HDCT/ASCT because of age ≥65 years (88. 7%), high hematopoietic cell transplantation-specific comorbidity index (HCT-CI) score ≥3 (32. 3%)  24  and/or previous ASCT (3. 2%) (Extended Data Fig. 2 ). Overall, 52 patients (83. 9%) received bridging therapy after leukapheresis at the investigator's discretion.
Grade 3 or worse prolonged cytopenias (defined as a grade ≥3 laboratory result of anemia, neutropenia and/or thrombocytopenia not resolved 30 days after axi-cel infusion) occurred in 23 out of 62 patients (37. 1%). At the time of data cutoff, 12 patients died, 5 of whom from lymphoma and 1 of unknown reason. Nonrelapse mortality (NRM) was recorded in six patients (9. 7%). All fatal adverse effects occurred late (beyond 2 months after axi-cel infusion) and were infections: two COVID-19, one aspergillosis, one mucormycosis, one sepsis and one perineal infection (Extended Data Table  3 ).
Before lymphodepleting chemotherapy was administered, reconfirmation of study eligibility, including positron emission tomography (PET)-positive disease, was required. Hence, disease assessment by PET was performed at screening, within 7 days before the start of lymphodepleting chemotherapy, as well as at 1, 3, 6, 9 and 12 months after the axi-cel infusion. Clinical examination (including ECOG performance status), blood sampling and laboratory tests (including complete blood count and serum chemistry) were also performed at screening, on the day of axi-cel infusion, daily between day 1 and day 10 after the axi-cel infusion, on day 14 and at 1, 3, 6, 9 and 12 months following the axi-cel infusion. Patients were followed-up for 3 years after the axi-cel infusion and could then consent to participate in a long-term follow-up for up to 15 years via DESCAR-T (ClinicalTrials.
gov ID NCT04328298), a French register of patients with malignant hemopathies eligible for CAR-T therapy. Safety was monitored continuously throughout the study. CAR-T cell toxicities such as CRS and ICANS were graded according to the American Society for Transplantation and Cellular Therapy grading system  45. All other Adverse effects were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE v. 5. 0).
We calculated CMR with exact Clopper-Pearson CI values, without adjustment for multiplicity. Patients without response assessment, due to any reason, were considered as nonresponders. We used the Kaplan-Meier method to estimate medians and 95% CI values for PFS, EFS, OS and DOR. If patients did not have an event at the time of the PFS, EFS and DOR analysis, they were censored at the date of the last disease assessment. For assessment of OS, alive patients were censored at their last follow-up date. Safety end points were assessed using descriptive statistics (counts and percentages). A subgroup analysis of CMR at 3 months was conducted for prespecified covariates (such as sex) and a Forest plot was provided. Collected data were entered using the Electronic Data Capture system from Ennov v.
8. 1 (Ennov). Sample size calculation was performed using EAST v. 6. 5 (Cytel). All statistical analyses were performed using SAS v. 9. 3 or higher (SAS Institute) and AdClin v. 3. 2. 2 or higher (AdClin). Abbreviations: HCT-CI, hematopoietic cell transplantation-specific comorbidity index; IPI, international prognostic index; LDH, lactate dehydrogenase; PMD, progressive metabolic disease; PMR, partial metabolic response; SD, stable disease; TMTV, total metabolic tumor volume. https://doi. org/10. 1038/s41591-023-02572-5 Extended Data Table  2  | Most frequent (in more than 10% of patients) adverse events in the modified full analysis set (N = 62).
Acknowledgements This study was funded by  Kite , a  Gilead  company. We thank the patients, their families and the study personnel involved in this trial. We also thank the ALYCANTE investigators,  P. Cony-Makhoul ,  E. Chareyre  and the LYSARC study team (Supplementary Table 1), including LYSA-IM and  LYSA-P , for their precious contribution. In addition, we thank  T. Rohban  and  M. El Hajj , from  Partner 4 Health  for providing medical writing support in accordance with Good Publication Practice (GPP3) guidelines.
Data availability This trial is currently ongoing. Requests for access to aggregate data and supporting clinical documents will be reviewed and approved by an independent review panel on the basis of scientific merit. The datasets generated and/or analyzed during the current study are not publicly available due to proprietary considerations. All data provided are anonymized to respect the privacy of patients who have participated in the trial, in line with applicable laws and regulations. Data requests pertaining to the manuscript may be made to the corresponding author (R. H. ; roch. houot@chu-rennes. fr). Requests will be processed within 12 weeks.