dataesr/scientific-paragraphs-categorization huggingface dataset:
The structural features underlying myosin force production are reviewed in 2. Analysis of the binding pocket of KNX-002. (a) Superimposition on the U50 subdomain of PfMyoA bound to KNX-002 (colored by subdomains, PfMyoA/KNX-002) and PfMyoA in the apo condition (black, PfMyoA/Apo). Small local accommodations are induced in the pocket by the binding of the drug, the most remarkable being a shift in Switch-2 (Sw-2) position. (b) Superimposition of PfMyoA/KNX-002 and PfMyoA in the pre-powerstroke (PPS) state (PDB code 6YCX, 3 on the U50 subdomain. The superimposition shows that cannot bind in PPS. In particular, a clash would occur if Switch-2 rearranges to close the back-door as found in PPS. Note in particular, the position F471 adopts in the PPS state. (c) Cartoon representation showing the ΦLL linker, a conserved element located after the Transducerβ7, in the U50.
Alignment of myosin sequences show the conservation of the ΦLL linker: PfMyoA; human fast skeletal muscle myosin 2 (SkMyo2_MYH2); human β-cardiac muscle myosin 2 (Cardiac_MYH7); human smooth muscle myosin 2 (SmMyo2_MYH11); human myosin 6 (Hs_Myo6); human myosin 5a (Hs_Myo5a); human myosin-1b (Hs_Myo1b); Dictyostelium discoidum myosin 2 (DdMyo2). Validation of the KNX-002 binding pocket. Representation of the differences existing in the KNX-002 binding pocket in PfMyoA and in β-cardiac myosin in the PR state (PDB code 6FSA, 4 , the two structures are aligned on the U50 subdomain of PfMyoA. The sequence differences in cardiac are represented as stick in grey (for detail see Fig. 3f). Residues mutated to investigate the pocket are represented in a red box. Raw data showing examples of fields of actin bound to surface immobilized PfMyoA.
AC K NO W L E DGE M E N T S We thank three anonymous reviewers for very helpful and insightful comments and suggestions. This study was supported by ANR JCJC grant ANR-16-CE35-0012-01 to SL, grants NIH/R01 GM122061-03 and NSF-DEBto MB, MEXT grant JP-24115001 to AS, and the ESB Cooperation Program, The Graduate University for Advanced Studies, SOKENDAI. This publication is funded in part by the Gordon and Betty Moore Foundation through grant GBMF10578 to MB. This work was initiated during a visit by SL and MB to SOKENDAI in January 2019. SL also wishes to acknowledge B. Duthie, L. Govaert, V. Luque, K. Lyberger and S. Patel for stimulating discussions while writing the first draft of the manuscript.
Funding information Agence Nationale de la Recherche , Grant/ Award Number:  ANR-16-CE35-0012-01 ;  Gordon and Betty Moore Foundation , Grant/Award Number:  GBMF10578 ;  Ministry of Education, Culture, Sports, Science and Technology , Grant/Award Number: JP-24115001;  National Institutes of Health , Grant/Award Number:  R01 GM122061-03 ;  National Science Foundation , Grant/Award Number: NSF-DEB-2011109.
In addition, anti-PD-1 checkpoint inhibitors have been correlated with the emergence of secondary T-NHLs in patients with other primary malignancies  3, 4. Moreover, clinical trials in patients with T-NHL have reported hyperprogression of individual T cell lymphomas, which were apparently still under PD-1 control, after anti-PD-1 treatment (NCT02631746 (refs. 5,6), NCT03075553 (ref. 7)). While all these clinical observations highlight the critical importance of inhibitory PD-1 signaling in T cell malignancies, the tumor-suppressive mechanisms of PD-1 remain unknown.
Article https://doi. org/10. 1038/s43018-023-00635-7 T cells with and without PD-1 function. Upon oncogenic T cell signaling, we detected enhanced glucose uptake in PD-1-deficient T cells compared to that of wild-type cells (Extended Data Fig. 1b ; P = 0. 0017). Moreover, extracellular flux analysis revealed a greater extracellular acidification rate (ECAR) in transformed ITK-SYK + PD-1 -T cells than in their premalignant ITK-SYK + PD-1 + T cell counterparts, whereas the oxygen-consumption rate (OCR) remained unchanged (Fig. 1g, h ; P < 0. 0001 and P, not significant (NS)). Because the increase in ECAR implies aerobic glycolytic conversion of pyruvate to lactate, we next measured the production of this metabolite. Lactate generation was enhanced in the transformed ITK-SYK + PD-1 -T cells compared to in their premalignant counterparts (Fig.
1i ; P = 0. 034). To study glucose metabolism in vivo, we used the glucose analog tracer [ 18 F] fluorodeoxyglucose ([ 18 F]FDG) and positron emission tomography (PET). The maximum [  18  F]FDG uptake in individual mice was measured in the spleens of both ITK-SYK CD4-CreERT2 and ITK-SYK CD4-CreERT2 ; Pdcd1 -/-mice (Fig. 2a  and Extended Data Fig. 2a ). The overall [  18  F]FDG uptake and maximum uptake in single voxels was significantly higher in ITK-SYK  ;Pdcd1 -/-animals than in PD-1-proficient ITK-SYK CD4-CreERT2 mice, demonstrating augmented glucose uptake in vivo (Fig. 2b ; P = 0. 005). To measure glycolytic conversion, we infused hyperpolarized [1-  13  C]pyruvate intravenously into tamoxifen-injected animals and compared [1-  13  C]lactate/[1-  13  C]pyruvate signal ratios in the spleens using hyperpolarized  13  C magnetic resonance spectroscopic imaging (MRI)  16.
4h  and Extended Data Fig. 4j ,k)  42, 43. To assess how these events would affect chromatin accessibility for transcription factor occupancy, we conducted a genome-wide assay for transposase-accessible chromatin using sequencing (ATAC-seq  44  ) and performed transcription factor footprint analysis  [45] [46] [47]. Intriguingly, in PD-1-deficient ITK-SYK-expressing T cells, we observed increased activity scores for the AP-1 family transcription factors c-FOS, FOSL1, FOSL2, c-JUN, JUNB and BATF (Fig. 5a, b ). Next, we tested whether the histones at the AP-1 family target gene sequences are hyperacetylated in PD-1-deficient ITK-SYK-expressing T cells by performing GSEA using the H3K27ac ChIP-seq dataset (Fig. 4h ; ChIP-Enrich analysis  48  ) and the transcription factor target gene sets from the ENCODE, JASPAR and ChEA databases  46, [49] [50] [51].
7c ; P = 0. 0309)  54. Moreover, uptake of the fluorescent glucose analog 2-NBDG was also significantly increased in PDCD1-mutant lymphoma cells (Fig. 7d ; P = 0. 03)  55. To perturb (PI3K)-AKT-mTOR signaling, glycolysis or ACLY activity in viable PDCD1-wild-type and PDCD1-mutant Article https://doi. org/10. 1038/s43018-023-00635-7 lymphoma cells, we incubated the six primary patient samples with respective small-molecule inhibitors of these pathways. Treatment with the mTORC1 inhibitor everolimus, which is currently in clinical trials for T-NHL therapy (NCT00918333 and NCT01075321), reduced viability and division of PDCD1-mutant cancer cells (Fig. 7e ). Similarly, inhibition of glycolysis with 2-DG or ACLY blockage with BMS-303141 also significantly impaired cell proliferation in PD-1-deficient lymphomas (Fig.
Discussion Here, we identified the key components of the PD-1 tumor-suppressor program in T cell lymphoma. Using a combination of genetic mouse models and primary human T-NHL samples, our results highlight the role of PD-1 as a critical gatekeeper molecule curtailing the glycolytic switch during neoplastic transformation of a T cell. We provide evidence that metabolic reprogramming via the (PI3K)-AKT-mTOR-HIF1α axis leads to induction of rate-limiting factors for glucose uptake   S73. Same experiment as in e. g,h, Differential transcription factor footprint analysis of ATAC-seq data from ITK-SYK + PD-1 + and ITK-SYK + PD-1 -cells after 3 h of in vitro incubation with BMS-303141 (5 µM, h) or DMSO (g). Spleen-derived eGFP + cells were sorted by flow cytometry from ITK-SYK CD4-CreERT2 and ITK-SYK CD4-CreERT2 ;Pdcd1 -/-mice on day 5 after tamoxifen injection, incubated in iACLY-or DMSO-containing medium and processed for ATAC-seq.
While our results identify PD-1 as a metabolic gatekeeper for T-NHL energy metabolism and induction of the epigenetic ACLY-AP-1 axis, several questions remain open. On the one hand, it is unclear whether unleashed ACLY activity in PDCD1-mutant lymphomas regulates acetyl-CoA-dependent metabolic pathways apart from histone acetylation, similar to de novo generation of lipids. On the other hand, it is undefined how PD-1 signaling regulates individual AP-1 family members. While our data show enhanced phosphorylation of key serine residues within the transactivation domains of c-JUN and c-FOS in the absence of PD-1 signaling, the precise mechanisms that underlie these effects remain to be uncovered. In this context, it will also be critical to elucidate how acetyl-CoA abundance can selectively enhance recruitment of AP-1 factors to AP-1-DNA binding sites and whether related pathways are also active in non-transformed T cells and during T cell exhaustion, which is frequently associated with chronic PD-1 signaling and dysregulated AP-1 activity  58.
cDNA was fragmented using the Nextera XT kit (FC-131-1096, Illumina), and only the 3′ end fragments were amplified using primers with Illumina P5 and P7 overhangs. Compared to ref. 60, the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve better cluster recognition. The library was sequenced on the NextSeq 500 platform (Illumina) with 75 cycles for cDNA and 16 cycles for barcodes and UMIs. Raw sequencing data were processed with DropSeq-tools (version 1. 12) using gene annotations from the Ensembl GRCm38. 87 database to generate sample-and gene-wise UMI tables  61. Downstream analysis was conducted using R version 3. 4. 452 and DESeq2 version 1. 18. 153 (ref. 62). Technical replicates with <100,000 total UMIs were excluded before differential expression analysis, and the remaining replicates were collapsed.
For PET-CT imaging, mice were injected with [ 18 F]FDG (~12 MBq), and tracer uptake was measured after 90 min using a preclinical Siemens Inveon PET-CT system. Splenic standardized uptake values were calculated using Inveon Research Workplace software. Hyperpolarized 13 C MRI of the animals was performed using a preclinical 7-T magnetic resonance scanner (Agilent-GE Discovery MR901 magnet and gradient system, Bruker AVANCE III HD electronics). The animals were injected with hyperpolarized [1-.
Data were acquired using a FACSCanto II or an LSRFortessa flow cytometer (BD Biosciences). FlowJo software was used for data analyses (FlowJo). In vitro. eGFP + T cells sorted by flow cytometry from tamoxifen-injected C57BL/6 and ITK-SYK CD4-CreERT2 ;Pdcd1 -/-mice were cultured in vitro in glucose-free DMEM (11966025, Thermo) supplemented with 1% dialyzed FCS (A3382001, Thermo). After 4 h, the cells were fixed in 4% PFA and processed as described above. An exemplified gating strategy is provided in Supplementary Fig. 1.
Reads were aligned to the mouse genome (mm9) and the Drosophila genome (dm6) using the Bowtie 2 alignment package  72. Aligned reads were sorted and indexed using SAMtools (version 1. 11)  73. Sambamba (version 0. 8. 0) was used to filter out unmapped, multi-mapped and duplicate reads  74. bamCoverage from deepTools (version 3. 3. 2) was used with spike-in (Drosophila) reads as a normalization factor to extract bigwig files for visualization of data  75. For differential analysis, peaks were called using the MACS2 package (version 2. 2. 7. 1)  76. DiffBind (version 2. 6. 6. 2) was used for differential analysis using the built-in spike-in (Drosophila) normalization option. GSEA for ChIP-seq peak data was performed using the ChIP-Enrich package (version 2. 0. 1)  48.
RNA-seq human and TCR-α and TCR-β sequence analyses RNA was extracted using the RNeasy Plus Micro Kit (74034, Qiagen). RNA quality was assessed with a Bioanalyzer 2100 (Agilent Technologies), and RNA was quantified using a Qubit RNA HS assay (Q3285, Thermo). cDNA libraries were generated using SMART-Seq version 4 (634891, Clontech) and Nextera XT (FC-131-1024, Illumina) and sequenced on an Illumina HiSeq with a read length of 150-bp paired-end reads. The sequencing reads were aligned using STAR (version 2. 4. 2), gene-specific transcripts were quantified using HTSeq (version 0. 6. 0), and differentially expressed transcripts were identified using DESeq2 (version 1. 30. 1)  62. MiXCR software was used (version 2. 1. 10)  80  to identify TCR sequences from RNA-seq data.
g, Phosflow analysis of serine 240/244 of S6 ribosomal protein in ITK-SYK + PD-1 -cells isolated from NSG mice treated with Torin-1 or vehicle. Cells were fixed and permeabilized immediately after isolation. h, eGFP percentage of viable lymphocytes in the peripheral blood of Torin-1 or vehicle-treated NSG mice, which were transplanted with 1,000 eGFP + FACS-sorted ITK-SYK + PD-1 -cells isolated from acutely induced ITK-SYK  ;Pdcd1 -/-mice. Blood was taken seven days after initiation of treatment. a,b,c,d,e,f, Representative results from 2 independent experiments with three biological replicates per group. g, Representative results from two out of the six animals analyzed. h, Representative results from two out of the thirteen animals analyzed. Extended Data Fig. 4  | PD-1 controls ACLY-dependent epigenetic reprogramming.
Acknowledgements We thank  K. Burmeister ,  A. Doddajjappanavar ,  V. Höfl ,  N. Prause ,  M. Arens ,  K. Pechloff ,  W. Lee ,  Z. Kurgyis ,  P. Gaulard  and  A. Wahida  for providing excellent technical assistance or helpful discussions and the Core Facility Cell Analysis at  TranslaTUM at Klinikum rechts der Isar of the Technical University Munich  for their support. We also acknowledge core facilities at  Northwestern  including the flow cytometry core and the  Skin Biology and Disease Resource Center. This work was supported by research grants from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  (project  ID 210592381 ,  SFB 1054 ; project  ID 360372040 ,  SFB 1335 ; project  ID 395357507 ,  SFB 1371 ; project  ID 369799452 ,  TRR 237 ; project  ID 452881907 ,  TRR 338 ,  RU 695/9-1 ,  RU 695/12-1 ), The  Leukemia & Lymphoma Society  and the  European Research Council  under the European Union's  Horizon 2020 Research and Innovation Programme  awarded grant agreement  834154  to J.
R. and grant agreement  820374  to F. S. F. S. was further supported by the  DFG  (project  ID 68647618 ,  SFB 824 ). J. C. was supported by the  National Institutes of Health  ( 1DP2AI136599-01 ), the  American Cancer Society  ( Research Scholar  Grant  RSG-20-050-01 ), the  Bakewell Foundation  and the  Leukemia Lymphoma Society Scholar Award  ( 1377-21 ). T. W. was supported by the  Cancer Research Institute  ( CRI4029 ) and is currently funded by  NIH-NCI  ( 1K99CA277586-01 ). J. D. was supported by  NIH-NCI  grants  F30 CA265107  and  T32 CA009560. Work in G. S. 's laboratory was funded by the  DFG  (project  ID 213249687 ,  SFB 1064 TP3 ; project  ID 329628492 ,  SFB 1321 TP13 ). D. B. was supported by  FNR CORE  grants ( C21/BM/15796788  and  C18/BM/12691266 ). M. M. was supported by research grants from the  National Institutes of Health  ( R35CA197628 ,  R01CA157644 ,  R01CA213138  and  P01CA233412 ) and the  Howard Hughes Medical Institute  ( HHMI-55108547 ).
Data availability All data are available from the corresponding authors upon reasonable request. For mouse data, RNA-seq, ATAC-seq and ChIP-seq data have been deposited in the Gene Expression Omnibus database under accessions GSE212832, GSE213180 and GSE183530. For human data, whole-genome sequencing, RNA-seq and ATAC-seq data for consenting patients are deposited in the database of Genotypes and Phenotypes under accession codes phs002456. v1. p1 (for previously published data from ref. 2) and phs003312. Raw data from RNA-seq and metabolic experiments can be found in Supplementary Tables  3 4 5 6 7 8 9 10 11 12 13 14. All other data supporting the findings of this study are available from the corresponding authors on reasonable request. Source data are provided with this paper.
scRNA-seq revealed that, although day 1 ESCs form a homogeneous population (cluster 0), day 18 spheroids are composed of three distinct cell types (Figure  2D ). Using known marker genes and comparisons with a scRNA-seq dataset from peri-implantation embryos,  24  we annotated cluster 2 as extraembryonic endoderm like (XEN-L) and cluster 3 as epiblast like (EPI-L) and found strong similarity of these two clusters to the corresponding cell types in the peri-implantation   S1. embryo (Figures  2E, S2A , and S2B; Table  S1 ). Cluster 1 was enriched for pluripotency markers and overlapped with untreated control ESCs grown under similar conditions (serum + Lif and switch to N2B27 + Lif); we thus referred to this cluster as ESC like (ESC-L) (Figures  2E, S2A , and S2C). Uniform manifold approximation and projection (UMAP) visualization of cell populations obtained in day 18 spheroids compared with those observed in peri-implantation embryos confirmed the identity of the three clusters (Figure  S2D ).
For the sake of clarity, we will hereafter call the elongated structures obtained with our protocol as AggreWell gastruloids (A-gastruloids). In addition to the 3 cell types detected in spheroids, scRNA-seq analysis of A-gastruloids revealed new cell populations derived from EPI-L cells (Figure  3C ). Comparing their transcriptional signatures with a dataset from post-implantation embryos  27  allowed us to identify cell types that correlated with primitive streak (cluster 4), nascent mesoderm (cluster 5), neuromesodermal progenitors (NMPs; cluster 7), neuroepithelium (cluster 8), and a very small number of cells with definitive endoderm identity (cluster 6) (Figures 3C, 3D, S4C, and S4D; Table  S1 ). A more detailed analysis revealed that the transcriptome of the A-gastruloids was most similar to the transcriptome of mouse embryos at embryonic day 6.
Transcriptional analyses demonstrate embryo-like developmental programs To further characterize the identity of cells present in the structures, we performed a scRNA-seq analysis in F2-and F5-gastruloids as well as in M-ELSs (Figure  5A ). Comparing the 18 resulting clusters with in vivo mouse transcriptomics datasets from gastrulation to early organogenesis  27, 35  confirmed the presence of cell populations derived from the three germ layers, including ectodermal lineages, mesodermal subtypes, and the gut endoderm (Figures 5A and S6A-S6C; Table  S1 ). Across the three   S1 , and Video S1. time points studied, progenitor cells progressively decreased, while more differentiated mesodermal and neural cell types gradually increased, as observed for their in vivo counterparts (Figure  S6D ). During mammalian trunk development, the bipotent NMPs give rise to spinal cord and paraxial mesoderm derivatives, with Sox2 and T acting antagonistically to specify these two cell fates, respectively.
To gain insight into the changes imposed by the first pulse of SUMOylation inhibition in re-establishment of the SUMO genomic profiles after recovery, we used a mass spectrometry (MS)-based proteomics approach to compare the set of endogenous SUMO2/3 substrates on day 1 and day 8. In total, we identified 1,296 SUMO2/3 sites mapping to 568 SUMO2/3 target proteins (Table  S2 ). Strikingly, no significant change was detected in the global SUMOylomes between the day 1 and day 8 conditions (Figure  S9E ). These data indicate complete recovery of the SUMO2/3 proteome soon after acute loss of SUMOylation, highlighting a previously unappreciated robustness of the SUMOylation/deSUMOylation equilibrium. Thus, relocation of pre-existing chromatin SUMO substrates toward new sites on the genome, rather than rewiring of the SUMO proteome, likely   S1.
Lastly, the slides were mounted in mounting medium containing DAPI (Fluoromount-G, Invitrogen). A-gastrulo sections were performed with this same protocol. The antibodies used were rat anti-Nanog (1:300, ThermoFisher Scientific #14-5761-80), goat anti-Sox17 (1:100, R&D Systems #AF1924), rabbit anti-Pou3f1 (1:100, Sigma #HPA073824), goat anti-T (1:100, R&D Systems #AF2085) or rabbit anti-T (1:100, Abcam #ab209655), rabbit anti-Snai1 (1:100, Cell Signaling #3879S), mouse anti-Pax6 (1:100, Abcam #ab78545), rabbit anti-Foxa2 (1:100, Cell Signaling #D56D6), mouse anti-Nestin (1:100, Millipore #MAB353), mouse anti-Tuj1 (1:100, Biolegend #8186), rabbit anti-Pax2 (1:100, Invitrogen #71-6000), mouse anti-Map2 (1:100, Sigma-Aldrich #M4403), rabbit anti-Sox10 (1:100, Abcam #ab264405), goat anti-Gbx2 (1:100, R&D systems #AF4638), rabbit anti-ZO1 (1:100, ThermoFisher Scientific #40-2200), mouse anti-Sox2 (1:100, Millipore #AB5603), Alexa Fluor 488 donkey anti-rat (Invitrogen #A21208), Alexa Fluor 546 donkey anti-goat (Invitrogen #A11056), Alexa Fluor 647 donkey anti-rabbit (Invitrogen #A31573), Alexa Fluor 488 goat-anti-mouse (Invitrogen # A11001), Alexa Fluor 488 donkeyanti-goat (Invitrogen # A32814) as well as Alexa Fluor 488 Phalloidin (1:400, Invitrogen #A12379).
The next day, samples were washed 4 3 15 min with 1 mL probe wash buffer at 37 C, and 2 3 5 min with 1mL 5X SSC-Tween at room temperature, then incubated in 1 mL amplification buffer for 5 min at room temperature. A mixture of 30 pmol of each hairpin (individually snap cooled beforehand) in 500 mL of amplification buffer was added to samples for an overnight incubation at room temperature in the dark. The next day, samples were washed 2 3 5 min, 2 3 30 min, 1 3 5 min with 1 mL 5X SSC-Tween at room temperature in the dark then stored at 4 C before imaging. Accession numbers for HCR probes used were Nanog (NM_001289828. 1, hairpin B1), T (NM_009309. 2, hairpin B3), En1 (NM_010133. 2 hairpin B4), Uncx (NM_013702. 3, hairpin B1). Hairpins B1 were labeled with Alexa Fluor 488 or Alexa Fluor 546, hairpin B3 with Alexa Fluor 647 and hairpin B4 with Alexa Fluor 488.
Pseudotime analysis Cells from the F5-gastruloid condition were selected and cluster computation was performed as described above. This F5-gastruloid UMAP was then used to determine trajectories for pseudotime with the Monocle3 R package  71  using the 'learn_graph' function with default parameters. The pseudotime start was chosen in the NMPs cluster. For heatmap representation, cells were ordered by their pseudotime, and a cubic smoothing spline (R function 'smooth. spline') was applied on the gene expression matrix. The z-scores of the smoothed values were plotted.
Expression levels of the SUMO machinery components The scRNA-seq dataset generated by Deng et al. 65  was used. Count tables were downloaded from GSE45719. Counts were normalized with the Seurat function 'NormalizeData'. Stages and genes of interest were selected for plotting. Methyl-seq DNA was extracted and purified from 2 million cells for each condition with the Quick-DNA Midiprep Plus Kit (Zymo Research D4075) following manufacturer's instructions. DNA was quantified with a NanoDrop ND-1000 (ThermoFisher Scientific). The NEBNext Enzymatic Methyl-seq Kit (New England Biolabs #E7120S) was used to prepare libraries for detection of 5-mC and 5-hmC. 200 ng of DNA from each sample were sheared to 275 bp fragments with an E220 Focused-ultrasonicator (Covaris) with the following settings: Duty Factor, 10% -Peak Incident Power, 175 W -Cycles per burst, 200 -Duration, 100 s.
Processing methyl-seq data Methyl-seq data were processed with the Bismark pipeline 72 using bowtie2 aligner  67  with the default parameters. Biological triplicates were merged and CG sites with at least 5 reads were kept for downstream analyses. Methylated and unmethylated CG sites were counted within predetermined windows (bin or interval) and a binomial test was used to compare different timepoints or regions. In a second approach, bins of 500 bp were compiled across the whole genome with a step of 200 bp. Next, replicates were classified as ''early'' (D1 and D3 replicates) or ''late'' (D8 and D10 replicates) with an inclusion criterion of at least 20 reads per bin in all combined conditions and a minimum of 1 read per time point. ''Early'' versus ''late'' conditions were compared using a binomial generalized linear model (logit) as follows: glm(data = test, cbind(test$meth, test$unmeth) $ test$condi, family = binomial(link = "logit")) where ''condi'' is the status of the sample (''early'' or ''late'').