dataesr/scientific-paragraphs-categorization huggingface dataset:
Interestingly, this rearrangement occurs also when GLC binds to ODH active site, where two molecules of GLC mimic the disaccharide (G3G) binding geometry. Finally, we can not exclude that the substrate-binding loop might also play a role in escorting monosaccharides toward the active site and, indeed, the presence in ODH-GLC of four GLC molecules bound to ODH active site and its funnel-shaped opening suggests the possible existence of a well-defined substrate diffusion pathway. The main forces responsible for G3G and GLC orientation in ODH active site are CH-π interactions with aromatic residues (Phe416, Phe421 and Trp430), which are rather conserved in ODH-related enzymes. On the contrary, in GDH class-I and GOX enzymes, such as AfGDH (PDB: 4YNT  [20] ) and AnGOX (1CF3  [19] ), the molecular determinants for the formation of the sugar-enzyme complex rely on specific hydrogen bonds between sugar hydroxyl groups and conserved polar residues from the active site (see Additional file 1: Figs.
Structure determination and refinement X-ray diffraction data of ligand-free ODH and ODH-G3G were collected at the Diamond synchrotron (Harwell, UK), beamline I24; data for ODH-GLC were collected at ELETTRA (Trieste, Italy), beamline XRD2. All datasets were collected at 100 K using a PILATUS detector. Data were indexed, scaled and integrated using the XDS package  [41]. Molecular replacement was carried out using MOLREP  [42]  from the CCP4 suite  [43]. The structure of AfGDH at 1. 78 Å resolution (PDB entry 4YNT  [20] , 36. 4% sequence identity to ODH) was used as the search model to calculate the initial crystallographic phases of ligand-free ODH, whose structure was then employed to obtain the initial phases for both ODH-GLC and ODH-G3G. Iterative structure refinement and model building were carried out using REFMAC5  [44]  and COOT  [45] , respectively, both implemented in the CCP4 suite.
S1 ), as well as on sophorose, gentiobiose, nigerose, kojibiose and isomaltose (see Additional file 1: Fig. S2 ). Because of different solubility, all sugar substrates were employed at a fixed concentration of 2. 5 mM, with the exception of GLC that was also used at 250 mM as a positive control of ODH reaction. The enzyme was added at a final concentration of 39. 7 nM to start the reaction (t = 0). DCIP reduction was followed over time by acquiring triplicates of the Abs 520 after an incubation of 2 and 5 min, as well as 3 and 19 h. Each data point was collected on three identical, independently prepared reaction mixtures. The same experimental setup was used to evaluate the kinetic parameters of ODH using GLC and G3G as substrates. GLC and G3G were tested at different concentrations ranging from 0 to 3000 mM and from 0 to 115.
5 mM, respectively. For each substrate concentration initial velocities were measured spectroscopically by monitoring the rate of absorbance decay at 520 nm over time for a total of 20 min in triplicate experiments. Initial rates were estimated by fitting the linear region of the time trace. Initial velocities measured at various substrate concentrations were fitted to the hyperbolic equation of Michaelis-Menten, which was applied to either all concentration points (G3G) or to points that looked unaffected by inhibition at extremely high substrate concentrations (GLC), as better detailed in the Results section. All data were fitted using the Kaleidagraph software package.
Analysis of ODH reaction products by LC-MS Liquid Chromatography was performed on a UHPLC Ultimate 3000RS (Thermo Scientific) coupled to a Charged Aerosol Detector (CAD Corona, Thermo Scientific) and an ISQ-EM mass spectrometer with heated ESI-interface (Thermo Scientific). The eluent was split 1:1 and the resulting flow from the LC to the MS was in all cases 0. 125 mL/min. The heated ESI was operated at 348 K in negative mode at -2 kV spray current, with a sheath gas flow of 23. 5 and an auxiliary gas flow of 2. 6 (arbitrary units). The capillary temperature was 523 K. UHPLC-ESI-MS data were acquired and analyzed with the Chromeleon software v7. 2. 10 (Thermo Scientific). An Acquity UHPLC BEH Amide column (2. 1 mm × 150 mm, 1. 7 mm, Waters, Milford, USA) was used for chromatographic separation of analytes.
Acknowledgements We acknowledge  Elettra Sincrotrone Trieste  for providing access to its synchrotron radiation facilities and we thank  Nicola Demitri  and  Annie Heroux  for assistance in using beamline XRD2. The authors would like to thank  Diamond Light Source  for beamtime and the staff of beamlines I24 for assistance with crystal testing and data collection, and synchrotrons  Soleil (FR)  and  ESRF (FR)  for the beamtime granted for crystal diffraction screening. Support from the  H2CU Center  to BV is acknowledged.
Funding This study was supported by an  ANS  grant from the  "Transform" INRAE Department  to  G. Sciara  , by the  French National Research Agency (ANR)  grants  FUNCLIPRO  ( ANR-19-CE43-0007-01 ) and  FUNLOCK  ( ANR-13-BIME-0002-01 ) and by the  Regional grant   CIRM-CF 2. 0 (APR Platforms PACA Region 2017 ). This work was supported by grants from  Sapienza : Grant  RM118164227A5FD6 , Grant  RM11916B838A3C38 , and the 2018 and 2019 grants " Sapienza Avvio alla Ricerca " to GC and to IF, respectively.
Microarray Experiment Microarrays were based on the Arabidopsis Genome Oligo Set version 1. 0 (Operon), manufactured as previously described  (Wellmer et al. , 2004)  and were kindly donated by Dr. Elliot Meyerowitz (Caltech, United States). Plant material, RNA extraction, antisense RNA labeling, raw data processing and data analyses are described in Supplementary Information. Microarray data was deposited in Gene Expression Omnibus (no. GEO: GSE164480, GSM5011985).
Accession Numbers Sequence data from this article can be found at the Arabidopsis Genome Initiative database with the following accession numbers: ABAP1 (At5g13060), Actin 2 (At3g18780), UBQ14 (AT4G02890), CDT1a (At2g31270), CDT1b (At3g54710), TCP16 (AT3G45150), EDA24-like (At1g23350), and ADAP/WRI3 (At1g16060).
ABAP1 Interacts With TCP16 and Regulates CDT1b Levels in the Male Gametophyte In leaves, ABAP1 negatively regulates mitotic cell divisions by repressing CDT1a and CDT1b expression, in an association with the transcription factor TCP24  (Masuda et al. , 2008). To determine the molecular mechanisms behind ABAP1's effects on the first mitotic division of pollen development, a mechanism similar to the one regulating mitotic proliferative cell divisions was tested. The TCP24 transcription factor is not significantly expressed during male gametogenesis, as determined in public microarray datasets; therefore, another TCP partner was searched. TCP16 acts regulating early pollen development, since reduced levels of TCP16 prevented PMI to occur, in a similar phenotype as the one observed in ABAP1 OE pollen  (Takeda et al.
Next, mRNA levels of CDT1a and CDT1b, two pre-RC genes whose expression levels are regulated by ABAP1-TCP24 in leaves, were investigated by qRT-PCR in mature anthers. TCP16 and two pollen developmental markers of pollen mitosis, Myb33  (Plackett et al. , 2011) and HAP2 (von Besser et al. , 2006) , were also included in the analysis (Figures  3B, C ). The results showed that in ABAP1 OE anthers, the expression level of TCP16 and CDT1a were normal, while the levels of CDT1b, Myb33 and HAP2 were significantly reduced (Figure  3C ). The reduction of CDT1b mRNA levels in pollen of ABAP1 OE compared to levels in control plants was also confirmed by whole mount in situ hybridization assays in anthers (Figure  3D ). The expression data corroborates the cellular analysis, supporting that the male gametophyte was arrested before PMI in ABAP1 OE plants, as CDT1b, Myb33, and HAP2 were down regulated, and that TCP16 might participate in this control since its expression level is normal.
FUNDING This research was supported by  Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro, Fundação de Amparo à Pesquisa do Estado de São Paulo ,  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior  ( CAPES/COFECUB SV. 622/18 ), and  Conselho Nacional de Desenvolvimento Científico e Tecnológico. LC was supported by  Ph. D. and postdoctoral fellowships from  CNPq. HM and FB were supported with graduate fellowships from  CAPES  ( Finance Code -001 ). HB was supported with a postdoc  CAPES/COFECUB  fellowship. HM received a grant from  FAPESP  ( 2009/53195-8 ). AH and PF receive support from a  CNPq  research grant.
DATA AVAILABILITY STATEMENT Sequence data from this article can be found at the Arabidopsis Genome Initiative database with the following accession numbers: ABAP1 (At5g13060), Actin 2 (At3g18780), UBQ14 (AT4G02890), CDT1a (At2g31270), CDT1b (At3g54710), TCP16 (AT3G45150), EDA24-like (At1g23350), and ADAP/ WRI3 (At1g16060). Microarray data was deposited in Gene Expression Omnibus (nos. GEO: GSE164480 and GSM5011985).
ACKNOWLEDGEMENTS LN2 is a joint  International Research Laboratory  ( Unité Mixte de Recherche IRL 3463 ) funded and co-operated by  Université de Sherbrooke (Canada)  and  CNRS (France)  as well as  INSA Lyon , ECL,  Université Grenoble Alpes  ( UGA ) as well as the  French national nanofabrication network RENATECH. The authors acknowledge FRQNT,  PROMPT  and  CNRS  for financial support. We acknowledge funding support from the  Natural Sciences and Engineering Research Council of Canada ,  NSERC CREATE TOP-SET  no. 497981 and MARS-CPV.
Multi-modal neuroimaging projects such as the Human Connectome Project (HCP) and UK Biobank are advancing our understanding of human brain architecture, function, connectivity, and their variability across individuals using high-quality non-invasive data from many subjects. Such efforts depend upon the accuracy of non-invasive brain imaging measures. However, 'ground truth' validation of connectivity using invasive tracers is not feasible in humans. Studies using nonhuman primates (NHPs) enable comparisons between invasive and non-invasive measures, including exploration of how "functional connectivity " from fMRI and "tractographic connectivity " from diffusion MRI compare with long-distance connections measured using tract tracing. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries.
, 2015 ; Kok et al. , 2016 ; Muckli et al. , 2015 ). The above considerations indicate that a deeper understanding of hierarchical processing in the cerebral cortex will require an accurate alignment of tract tracing and neuroimaging results, as we propose here. Here, we describe our collaborative nonhuman primate neuroimaging and neuroanatomy project (NHP_NNP). Building on multiple pairwise collaborations between our laboratories, the NHP_NNP was launched in 2018 as an international collaboration across five laboratories (RIKEN/KU, WUSTL, SBRI/IoN, OU); UMinn joined in 2020. The project's goals include (i) implementing a high resolution HCP-style data acquisition and analysis approach in 100 macaques and marmosets; (ii) developing a comprehensive quantitative invasive tract tracing database accurately mapped onto MRI-based volumes and surfaces; (iii) comparing imaging-based functional and structural connectivity measures to "ground-truth " anatomical tract tracers; (iv) analyzing cytoarchitecture quantitatively via immunohistologically-defined cell types to map brain areas; (v) studying over 100 macaques and marmosets, to compare individual variability in social behavior, genetics and brain organization; and (vi) studying some macaque animals using a unique ultra-high resolution 10.
Overview of NHP_NNP We are currently collecting in vivo neuroimaging data in large samples of macaques and marmosets, and we plan to acquire both neuroimaging and anatomical data from a smaller number of macaques. The in vivo data, including behavioral, genetics, and high-quality brain imaging, are mainly acquired and preprocessed at the labs of Takuya Hayashi and Chihiro Yokoyama in RIKEN. The analysis of in-vivo brain imaging is carried out by Hayashi's lab and David Van Essen's lab at WUSTL. Anatomy studies are carried out in Henry  Kennedy's lab (in SBRI, Lyon and IoN, Shanghai)  with David Van Essen's lab at WUSTL contributing to data analysis. Some animals in Lyon and Shanghai are also scanned with high-quality brain imaging by the same protocol as in RIKEN. Stephen Smith's lab at OU is engaged in developing a core part of in-vivo image preprocessing and higher-level modeling of connectivity using in-vivo brain imaging data.
David Van Essen's lab at WUSTL is involved in establishing cortical surface mapping of multimodal data, surface-based analysis, and registration across species. The CMRR at UMinn (Director, Kamil Ugurbil) and the labs of Jan Zimmermann, Sarah Heilbronner, Damien Fair and Essa Yacoub carry out NHP imaging on a 10. 5T scanner  ( Yacoub et al. , 2020 , this issue). The animal experiments in RIKEN/KU SBRI/IoN, and UMinn were approved by the relevant local animal experimental committee. The project aims to obtain high quality in vivo multimodal data (i. e. brain MRI, social behavior, and genetics) from three genus (and five species): ∼100 macaques (rhesus, Macaca mulatta ; crab-eating monkeys, Macaca fascicularis ; and Japanese rhesus, Macaca fuscata ), ∼100 common marmosets ( Callithrix jacchus ) and ∼10 owl monkeys ( Aotus lemurinus ).
All data except for those used in anatomical studies are being acquired in living animals. This is large compared to most published NHP imaging studies, which typically face practical limitations related to ethical and economic reasons, but is modest relative to large-scale human neuroimaging endeavors such as the HCP and UK Biobank. Individual variability is much lower in monkeys compared to humans (see Fig. 2  ), which in some respects alleviates the need for large sample sizes. At RIKEN, our strategy for macaques is to obtain data while macaques are hosted for a short period at our imaging facilities at RIKEN by generous arrangements with animal vendors. We plan to obtain data from adult macaques aged 3 years or older, sampling both sexes equally. For neuroanatomy, the project aims to investigate connectivity of a total of 139 cortical areas in each hemisphere by injecting retrograde tracers.
We already have data from 52 cortical injections (51 neocortical injections + hippocampus); thus additional data will be obtained for ∼ 90 injections. The tract tracing experiments are in a large part carried out in female cynomolgus monkeys. For marmosets, RIKEN Kobe has a breeding colony of over 120 marmosets, and we aim to obtain data from all animals in this colony once they reach maturity (age 2 years or older). In addition, we also study 10 owl monkeys generously provided by Prof. Masahiko Takada, Kyoto University Primate Research Institute.
, 2014 ; Glasser and Van Essen, 2011 ). Fat suppression was applied to T1w scans to attenuate signals from bone marrow and scalp fat, thereby reducing potential confounds in estimation of cortical outer surface contours. Since movement during structural image acquisition can cause blurring and imaging artifacts, we acquire all structural (T1w and T2w) and diffusion images under deep anesthesia (see Section 2. 5 ). Data preprocessing used an HCP-NHP pipeline  ( Autio et al. , 2020b ; Donahue et al. , 2016 ) , adapted to each NHP species by modifying many parts of the HCP pipeline  ( Glasser et al. , 2013 ). The major modifications are listed in Table  2  including: 1) measurement-related features and 2) neurobiological differences across primate brains. Measurement-related features include variations in B 0 and B 1.
b  Gordon et al. (2016). c  Glasser et al. (2016a Glasser et al. ( , 2016b   l Neuroimaging protocols are from the young-adult Human Connectome Project (YA-HCP)  ( Van Essen et al. , 2012c ) , Yerkes for chimpanzee study  ( Donahue et al. , 2018 )  and NHP Neuroimaging & Neuroanatomy Project (NHP_NNP) for macaque and marmosets  ( Autio et al. , 2020b ). The imaging protocols of NHP_NNP are available at https://brainminds-beyond. riken. jp/hcp-nhp-protocol/. blades in anterior temporal and occipital cortex in NHP are addressed by feeding a white matter skeleton into the FreeSurferPipeline, allowing accurate estimation of the white matter surface using mris_make_surface  ( Autio et al. 2020b ). A species-specific cortical thickness maximum is used in mris_make_surface when estimating pial surface to prevent underestimation of cortical thickness in these upscaled datasets.
5 for anesthetic protocols). We confirmed that our macaque protocol provides higher sensitivity of functional connectivity (e. g. default-mode network) even in 15 min duration as compared to other PRIME-DE open data, and increasing the scan duration to a total of 102 min further enhances the quality of the fMRI as shown in Fig. S12  in  Autio et al. (2020b). The MION protocol in awake animals likewise involves multiple fMRI runs performed across days to maximize the total amount of fMRI data. Overall we plan to acquire around 1 million resting state fMRI volumes in the macaque, 1/5th of the 5 million resting state fMRI volumes acquired in the YA-HCP. The preprocessing of resting-state fMRI data uses a corticalsurface approach to maintain the spatial fidelity of functional images  ( Autio et al.
, 2020b ). Motion correction uses the mcflirt_acc. sh script in the HCP-NHP pipeline, which outperforms registration to reference volume. This showed robust results compared with mcflirt that quickly calculates 4D volume registrations but often results in motion estimation errors particularly in the round-shaped marmoset brain or low SNR data like MION fMRI. The fMRI data is corrected for distortion with spin-echo (SE) EPI and TOPUP using an optimized warp resolution ( Table  2  ). SE-EPI data is used so that the registration-based internal correction model does not have to consider areas of differential signal dropout inherent in the gradient echo images. The two opposing phase SE-EPIs will be obtained, which should be distorted locally by the same amount but in the opposite direction along the encoding axis.
Artifacts and nuisance signals are removed using automated classification (FIX) of independent component analysis (ICA)  ( Glasser et al. , 2018 ; Salimi-Khorshidi et al. , 2014 ). FIX is trained for each species (macaque, marmoset) and protocol (BOLD or MION fMRI) and achieves high accuracy of classification  ( Autio et al. , 2020b ). A venous sinus map in the standard space was created for each species to be used as a feature for the FIX classifier ( Table  2  , Fig. 3  ). Fig. 4  shows exemplar results for functional connectivity seeded from the posterior cingulate/precuneus cortex (pC/PCC), i. e. 'default mode network (DMN)'  ( Raichle et al. , 2001 ; Vincent et al. , 2007  ). An initial test fMRI dataset in anesthetized macaques in NHP_NNP (Mac30BS, N = 30) was used for analysis of functional connectivity, while the human data was from young adult HCP subjects ( N = 210).
Behavioral state during functional neuroimaging -sedated, awake resting and task-performing A critical issue for comparing brain function between species is the harmonization of 'brain state' during tests. What is the optimal 'state' for recording resting-state functional MRI in NHP and humans? Arousal state is known to affect the amount of global signal in the brain  ( Liu et al. , 2017 ( Liu et al. , , 2018b ; ; Power et al. , 2017 ; Tagliazucchi and Laufs, 2014 ; Yeo et al. , 2015 ) , though it is not yet well understood to what extent this effect is due to physiological confounds. In YA-HCP, resting-state fMRI data was acquired after subjects were instructed to remain awake, however, a substantial number of subjects fell asleep because of the relatively long scan time (15 min). For species comparison, it is ideal to obtain the NHP resting-state fMRI scans in the awake condition, however, this is practically challenging to achieve in a large cohort, since awake imaging requires an invasive surgery to implant a head post, and laborious training for animals to adapt to noisy fMRI environments.
A macaque subcortical atlas and weighted subcortical-cortical connectivity matrix Each retrograde tracer injection case contains labeled neurons in up to 30 subcortical nuclei as well as the extensive cortico-cortical connectivity patterns discussed in the preceding section. However, quantitative analyses of the subcortical connectivity patterns are hindered by the lack of an atlas that accurately and fully delineates the boundaries between all identified nuclei. We propose to generate such an atlas plus an associated connectivity database that provides counts of neurons in individual subcortical structures down to and including the brain stem. To this end, we have sectioned a macaque brain (600 coronal sections at 40 microns) and processed adjacent sections for parvalbumin, calbindin,  NeuN and Nissl (Cresyl Violet).
Imputing connectivity Some connectivity databases, such as an influential connectome study of the mouse  ( Oh et al. , 2014 ) , use computational methods to infer connectivity for cortical areas that did not receive a precisely restricted tracer injection or for other technical reasons. A robust imputation method offers the prospect of predicting the strength of missing links and better approximating a weighted structural connectome of the macaque brain. Our approach is informed by recent studies that characterize connection weight patterns in mesoscale connectomes  ( Horvát et al. , 2016 ; Markov et al. , 2011 Markov et al. , , 2014a ; ; Theodoni et al. , 2020 ). Thus, inter-areal networks are not random graphs, but complex networks with distinctive structural features. If weight-distance relations of areal connectivity are predictive, could injection in a subset of areas be used to impute the connectivity of the full matrix? Zoltan Toroczkai and his students in collaboration with Henry Kennedy lab has developed a machine learning framework that shows the high predictability of cortico-cortical pathways in the macaque  ( Molnar et al.
However, the distance of connections is estimated by group-averaged streamline length of tractography whose reliability still needs further study. Both the DTe and DTi showed high sensitivity (99% and 100%, respectively, at a threshold of 10 -6 ), which suggests that HCP-style in vivo NHP data acquisition can provide connectivity estimates comparable in fidelity to the higher resolution attainable with postmortem scans. But both approaches need significant improvement particularly in specificity (0. 8% and 0. 4%, respectively) to better estimate ground-truth anatomical connectivity. Correlation based on true positives reveals the agreement of the connection weight between tractography and tract tracing for the connections detected by tract tracing, but it is important to also consider false negatives and false positives in a receiver operator curve type analysis  ( Donahue et al 2016 ).
Also note that Fig. 6  C and D show an orthogonal regression that accounts for the variance in empirical values of TT and DTe or DTi, thus, we may need to evaluate whether the refinement of tracer mapping ('dense mapping' and 're-parcellating') described in Section 3. 1 can further improve linear fitting. We are mapping tracer and neuroimagingbased connectivity in the same animal in order to resolve cross-subject variability in the cortical connectivity  ( Markov et al. , 2011 ). We also hope to facilitate algorithm development and evaluation of in vivo tractography by making the datasets publicly available. For example, one potential avenue is to invoke geometric constraints on the trajectories of neighboring fiber bundles in gyral blades allowing unbiased sensitivity over the surface and reliable dense diffusion connectivity.