dataesr/scientific-paragraphs-categorization huggingface dataset:
Experiments The aim of this Section is to experimentally validate the analysis conducted in Section 4 about the JS/WS prediction of the proposed prototpe. We first address the question of whether a model with planar assumptions is adequate to model our prototype or not. Then, we focus on the experimental reconstruction of singularity curves that delimits the prototype range of motions, namely 𝑇 2𝑎 and 𝑇 𝐿. For each curve, we compare simulation with experimental data to assess the accuracy of our prediction. To acquire experimental data, we employed the experimental setup illustrated in Fig. 8. A fixed camera was used to record images of the Charuco Board marker attached to the EE, and these pictures were then processed using an OpenCV Python library  [47]  to reconstruct the EE pose. For each reconstructed pose of the EE, the motors' angular position was also logged, assuming the motors' PID controller steady-state error to be negligible.
JS and WS verification In this subsection, we verify the correctness of the JS/WS simulation we performed in Section 4. To do this, we move the robot in several stable configurations with EE positions equally distributed over the WS, and we store motor angles and EE position at each configurations. We also seek to reach exterior WS limits, by moving the EE as far as possible from the motor axis, and inner WS limits, by moving the EE toward the motor axis. 0 Fig. 9  displays the superimposition between experimental data and simulations. Experimental joint angles are superimposed over the computed JS, while measured EE positions over the theoretical WS. The model with planar displacements assumptions is used in Figs. 9(a), 9(b) and the full spatial model is employed in Figs. 9(c), 9(d). By looking at Fig.
CglD promotes (but is not required for) gliding motility on deformable substrata As the cglD locus was originally identified through its conditional importance for gliding motility, we next set out to reconcile the disparate datasets in which CglD has been postulated to either be required  14  or not  15  for gliding motility. We first compared gliding-dependent swarm-edge flare formation for WT and ΔcglD cells across diverse matrices, each characterized by a distinct elastic modulus (i. e. resistance to deformation). For agar (1. 5% w/v), carrageenan (1. 5% w/v), and gellan (0. 6% w/v) matrices, gliding flares were clearly detected for both strains already after 7 h (Fig. 4A ). Incidentally, flares on gellan were considerably more prominent/noticeable compared to those on either carrageenan or (canonical) agar substrata (Fig.
The lower and upper boundaries of the plots correspond to the minimum and maximum values of the dataset, with the 25 th and 75 th percentiles displayed (dashed black lines). The median (solid black line) of each dataset is indicated. Asterisks denote datasets displaying statistically significant (*) differences in distributions (p < 0. 0001) between WT and ΔcglD cells, as determined via unpaired two-tailed Mann-Whitney tests. (C) Violin plots of reversal events per minute for tracked WT and ΔcglD cells (see Panel B) on pads solidified with agar, carrageenan, or gellan across 3 biological replicates (agar: nWT = 1534 events, nΔcglD = 1237 events; carrageenan: nWT = 233 events, nΔcglD = 272 events; gellan: nWT = 1826 events, nΔcglD = 627 events). The lower and upper boundaries of the plots correspond to the minimum and maximum values of the dataset, with the 25 th and 75 th percentiles as well as the median not distinguishable due to skewing of the data by non-reversing cells.
Phenotypic Analyses For all phenotypic analyses, 5 µL of cells resuspended in TPM (at an optical density at 600 nm [OD600] of 5. 0) were spotted on various substrata. Gliding-flare observations were acquired with an Olympus SZX16 stereoscope equipped with an ILLT base and UC90 4K camera, using CellSens software (Olympus). For gliding-flare analysis, cells in TPM were spotted on CYE 1. 5% (w/v) agar, 1. 5% (w/v) carrageenan, or 0. 6% (w/v) gellan plates and incubated at 32 °C for 7 h. Flares were imaged using the SDF PLAPO 2×PFC objective, with 6. 3× zoom, and a fully-open aperture. The illumination wheel was set halfway between the brightfield and empty slots for optimal cross-illumination of the sample. Image acquisition was carried out using linear colour, 50 ms acquisition time, 16. 6 dB gain, and a white balance calibrated against an empty zone of the plate.
For T4P-dependent swarm-spreading and fruiting body analysis, images were acquired with an Olympus SZ61 stereoscope with an ILLT base. Swarm spreading was captured at 0. 67× zoom, using dark-field illumination, while fruiting bodies were captured at 32× zoom, using full oblique illumination. For swarm-spreading, cells were spotted on CYE 0. 5% (w/v) agar plates, while for fruiting body formation, cells were spotted on CF (0. 15% casitone [w/v], 10 mM MOPS, 1 mM KH2PO4, 8 mM MgSO4, complemented with 0. 02% (NH4)2SO4 and 2% Na3C6H5O8), with 1. 5% or 0. 5% (w/v) agar for phenotype plates. Swarm-spreading and fruiting-body plates were imaged after incubation for 72 h at 32 ºC. Swarm diameters were measured using CellSens software.
Calculation of cell gliding speeds was performed using the MicrobeJ plugin for FIJI  53  , while reversal events (switching of cell gliding direction) were manually counted. To probe for AglZ-YFP cluster fluorescence on agarose pads, imaging was conducted using an in-house-made aluminum monolithic microscope equipped with a 1. 49 NA/100× objective (Nikon). Imaging was performed with an iXon DU 897 electron-multiplying charge-coupled device (EMCCD) camera (Andor Technology), with illumination achieved using a 488-nm diode-pumped solid-state (DPSS) laser (Vortran Stradus). Sample positioning was carried out using a P611 three-axis nanopositioner (Physik Instrument). LabView (National Instruments) was used to program instrument control and integrate control of all components. From these datasets, kymographs were generated using the "Kymograph Builder" function in FIJI.
Tertiary structure homology detection and protein modeling Structural homologues to CglD (MXAN_0962) were identified via fold-recognition analysis by HHpred  74  against protein structures in the PDB_mmCIF70 and PDB_mmCIF30 databases of structures deposited in the Protein Data Bank. Top hits were based on the highest probability scores. Tertiary structure modelling of CglD was carried out using the ColabFold pipeline to run AlphaFold with default settings  75, 76. The highest-confidence CglD model structure was used to generate structural alignments with known proteins using TM-align  77. All structure figures were created with PyMol.
This was followed by use of a band-pass filter to highlight the fluorescent particles. A custom PIV algorithm was then used to measure the displacement of the particles. Lastly, following established methods  79, 80  , traction was reconstructed from the displacement field of the gel. The same analysis method was used for analyzing both the dilute-cell and cell-layer samples. The brightfield images were used to track the motion of individual cells in the dilute regime. The original images were processed, binarized, and segregated, so that the center of mass for each cell could be located. Cell motion was then tracked using the Matlab version of the Particle Tracking Velocimetry (PTV) code developed by Blair and Dufresne  81. Using the positions of the cells in every frame, we calculated their speeds.
Bead tracking and video analysis Cells of M. xanthus with surface-deposited beads were imaged for 3 min, with images captured every 10 s. Movies were analyzed using a custom MATLAB tracking code. Prior to tracking beads, the code filtered and subtracted the background of the image from the cell-attached bead. An internal MATLAB centroid function was then used to identify the center of the bead and converted the x,y pixel values of the center of the bead in each frame to microns. The x,y position values of the bead center were then used to compute bead displacements to identify and extract individual motor-driven bead runs. Injection of nigericin (20 µM), a pH gradient/proton-motive force inhibitory drug, into the flow chamber with WT cells was also carried out; this drug was used to disable the molecular motors and reduce bead motion without impacting motor force production allowing us to determine a threshold for a bead run.
Analysis of GST affinity chromatography data via mass spectrometry In duplicate, C-terminal amino acids 800-1218 of M. xanthus GltD (formerly AgmU), fused to glutathione-S-transferase (GST), were previously heterologously expressed in E. coli, purified, and used as bait to pull down potential interactors from M. xanthus whole-cell lysate (with GST by itself used as a control to identify non-specific binders). Pulldown samples were then digested with trypsin, with raw tandem mass spectra acquired at the UC Berkeley Proteomics/Mass Spectrometry Laboratory using a Thermo LTQ XL mass spectrometer  12. Herein, the raw MudPIT mass spectra from these GST-affinity pulldowns were processed using Thermo Proteome Discoverer software (v2. 4. 1. 15) with the SEQUEST search engine at the Concordia University Centre for Biological Applications of Mass Spectrometry.
Database searches were carried out against the UniProt Myxococcus xanthus DK 1622 proteome database (UP000002402, v2017-10-25). The enzyme for the database search was chosen as trypsin (full), with maximum missed cleavage sites set to 3. Mass tolerances of the precursor and fragment ions were set at 1. 0 Da. Dynamic modifications on Methionine (oxidation, +15. 995 Da), protein N-terminus (Acetyl, +42. 011; Met-loss, -131. 040; Met-loss-Acetyl, -89. 030) and static modifications on Cysteine (carbamidomethyl, +57. 021 Da) were allowed. Only peptides with high confidence were reported. The XCorr confidence thresholds were applied with the factory default values, as 1. 5 for z = 1, 2. 0 for z = 2, 2. 5 for z = 3 and 3. 0 for z >=4 ions. To stringently identify "MXAN_" proteins pulled down via the GltD bait, non-specific hits pulled down with the GST-alone controls were first subtracted from GltD-GST hit lists, followed by removal of hits not meeting the quality threshold (2.
Statistical analysis For all comparisons of dataset distributions (Figs. 3D, 4B, C, 5A, B, 6A, C, E, F, ) analyses of statistical significance were carried out via unpaired two-tailed Mann-Whitney test. Differences in mean values for swarm diameter were evaluated for statistical significance using a Wilcoxon signed-rank test performed relative to the reference value of "100" for WT samples (Fig. 3B ). All statistical analyses were carried out in GraphPad Prism (version 8) at a confidence interval of 95% (P < 0. 05).
ACKNOWLEDGMENTS The authors would like to thank  A. Roussel  and  R. Vincentelli  ( CNRS-Aix-Marseille University, Architecture et fonction des macromolécules biologiques ) for CglD protein used to generate pAb; the  Confocal Imaging Facility , a  Nikon Center of Excellence , in the  Department of Molecular Biology at Princeton University  for instrument use and technical advice;  G. Laevsky  in particular for his support and suggestions. Funding:  Natural Sciences and Engineering Research Council of Canada , Discovery grant  RGPIN-2016-06637  (STI)  Natural Sciences and Engineering Research Council of Canada , Discovery grant  RGPIN-2023-05576  (STI)  Natural Sciences and Engineering Research Council of Canada , Discovery grant  RGPIN-2020-07169  (AJE)  Natural Sciences and Engineering Research Council of Canada , grant  EQPEQ/472339-2015  (AJE)  Banting Research Foundation ,  Discovery  Award  2018-1400  (STI)  Canadian Institutes of Health Research postdoctoral fellowship  ( STI) Aix-Marseille University AMIDEX Excellence Program (STI)   PROTEO , The  Quebec Network for Research on Protein Function, Engineering , and Applications studentship (NYJ, FS)  European Research Council ,  Advanced Grant JAWS  (TM)  Bettencourt-Schueller Foundation ,  Coup d'élan pour la recherche  française 2011 (TM)  National Science Foundation  grant  CAREER PHY-0844466  (JWS)  National Science Foundation  grant  PHY-1734030  (EH)  Fondation ARC  studentship (LMF)  Glenn Centers for Aging Research award  (BPB)  National Institutes of Health  award  P50 GM071508  (BPS)  National Institutes of Health  grant  GM20509  (DRZ)  National Institutes of Health  grant  GM129000  (BN)  Indian Institute of Technology Hyderabad seed  grant (GS)  Department of Science and Technology, India   INSPIRE grant  (GS).
( D ) D Polymertropism response determined by calculating the average aspect ratio of a swarm over time, normalized to the WT control strain run at the same time. on time and normalizing it on the WT. Both ΔcglD and ΔcglB displayed significantly different dataset distributions (*) compared to the values for WT, as determined via Mann-Whitney test (p < 0. 05). Indicate figure parts with bold capital letters: (A), (B), etc.
(G) Directionality of trafficked Agl-Glt complexes via TIRFM (of AglZ-YFP) on chitosan-coated glass surfaces for WT and ΔcglD (n = 44 and 43 cells, respectively) strains. "Front" and "back" are defined as cell poles with high and low AglZ-YFP fluorescence intensity, respectively. For Panels B-G, the lower and upper boundaries of the plots correspond to the minimum and maximum values of the dataset, with the 25th and 75th percentiles displayed (dashed black lines). The median (solid black line) of each dataset is indicated. Asterisks (*) denote datasets displaying statistically significant differences in distributions (p < 0. 05) between various strains or conditions, as determined via unpaired two-tailed Mann-Whitney tests.
Bayesian estimate The Bayesian framework allows obtaining the posterior distribution of the neutrality index ( ρ) for a given trait. Even though ρ is estimated independently for each trait of interest in the maximum likelihood framework (previous section), here we generalize to K traits co-varying along the phylogenetic tree using the BayesCode software  (Latrille et al. , 2021). Trait variation along the phylogenetic tree is modeled as a Kdimensional Brownian process B (1×K) starting at the root and branching along the tree topology  (Huelsenbeck & Rannala, 2003; Lartillot & Poujol, 2011; Lartillot & Delsuc, 2012; Latrille et al. , 2021). The rate of change of the Brownian process is determined by the positive semi-definite and symmetric covariance matrix between traits Σ (K × K). The off-diagonal elements of Σ are the covariance between traits, and the diagonal elements are the variance of each trait, thus corresponding to σ 2 B (see section S2.
2A  and section S1. 1 for theoretical formulation). We assumed a trait with a narrow-sense heritability of h 2 = 0. 2 and computed the theoretical V E accordingly (see section S1. 1). Assuming a diploid panmictic population of size N e = 50 at the root of the tree, and with non-overlapping generations, we simulated explicitly each generation along an ultrametric phylogenetic tree. For each offspring, the number of mutations was drawn from a Poisson distribution with mean 2 • µ • L, with the mutation rate per generation µ. From the empirical mammalian dataset (see next section), we computed Trait selection from within and between-species variation an average nucleotide divergence from the root to leaves of 0. 18 and average genetic diversity of 0. 00276. We scaled parameters in our simulations to fit plausible values for mammals.
Empirical dataset We analyzed a dataset of body and brain masses from mammals. The log-transformed values of body and brain masses were taken from  Tsuboi et al. (2018). We removed individuals not marked as adults and split the data into males and females due to sexual dimorphism in body and brain masses. We discarded species with only one representative sample. The mammalian nucleotide diversity was obtained from the Zoonomia project  (Genereux et al. , 2020) , with nucleotide divergence obtained on a set of neutral markers in  Foley et al. (2023) , and with nucleotide diversity measured as heterozygosity in  Wilder et al. (2023). We also analyzed a dataset of primate species, with the nucleotide variation obtained from  Kuderna et al. (2023)  and the quantitative trait variation also from  Tsuboi et al.
Results against simulations The inference framework was first tested on independently simulated datasets matching an empirically relevant mammalian empirical regime (see Materials and Methods). Under constant population size (N e ) and constant mutation rate (µ) across the phylogenetic tree (fig. 3 , top row), we found no false negative for simulations of stabilizing (P[ ρ < 1] > 0. 975; blue in fig. 3 ) or diversifying (P[ ρ > 1] > 0. 975; red in fig. 3 ) selection. For simulations under neutral evolution, 77% of those were correctly identified (0. 025 ≤ P[ ρ > 1] ≤ 0. 975; yellow in fig. 3 ), while 21% and 2% were wrongly detected as stabilizing or diversifying selection, respectively. Once we introduced fluctuating N e and µ (Fig. 3 , bottom row), our ability to identify simulations under either diversifying or stabilizing selection remained the same with all cases detected correctly.
Results on empirical data For mammalian body and brain mass, we obtained male ( ) and female ( ) trait variations. Combined with nucleotide diversity and divergence, we estimated ρ and posterior probabilities of diversifying selection under different assumptions for trait heritability as shown in the Table  1. Assuming complete heritability, brain mass was found to be under diversifying selection with posterior probabilities of 0. 0 for both males and females. If we assumed that heritability (h 2 ) of body mass was uniformly distributed between 20% and 40%  (Hu et al. , 2022) , posterior probabilities of diversifying selection became 0. 635 for males and 0. 324 for females. Mammalian brain mass was found to be under diversifying selection with posterior probabilities of 0. 877 for males and 0.
972 for females when complete heritability was assumed. Assuming a uniform distribution between 20% and 40% for heritability led to posterior probabilities of diversifying selection of 1. 0 for both males and females. We also analyzed a similar dataset for body mass focusing this time only at Primates (Table  1 ). For primates body mass, we found posterior probabilities of diversifying selection of 1. 0 for males and 0. 914 Traits simulated under stabilizing selection (blue), under a neutral evolution (yellow), and under a moving optimum (red). Histogram of ratio of between-species trait variation ( σ 2 B ) over within-species trait variation σ 2 W with ρ = σ 2 B / σ 2 W estimated from each simulated data (left) and probabilities of ρ being greater than 1 (right). Effective population size (N e ) and mutation rate (µ) were either constant (top row), or fluctuating as a Brownian process along the phylogenetic tree (bottom row).
, 2008). Third, the genetic architecture of the trait was assumed to be constant across the phylogenetic tree, whereas it might actually be variable among individuals and species  (Huber et al. , 2015; Tung et al. , 2015). Such an assumption can theoretically be relaxed and changes in genetic architecture along the phylogenetic tree could jointly be estimated  (Arnold et al. , 2008; Gaboriau et al. , 2020; Hohenlohe & Arnold, 2008; Kostikova et al. , 2016). Finally, our Bayesian estimation could integrate uncertainty from the estimation of genetic variation, using sequences as input instead of estimated values of nucleotide diversity and divergence. From an empirical point of view, our method required integrating genomic and trait variation, which could reduce the possible datasets to be used.
However, such datasets will become more and more accessible and we showed the applicability of our method by applying it to the illustrative example of mammals brain and body mass. Because our test was also based on several assumptions that might not hold on empirical data, we also provided a table containing the main assumptions and their consequences on the neutrality index and the test that can be performed (Table  2 ). For example, at the primate scale, the evidence for ρ > 1 does not necessarily imply that the brain mass was evolving under diversifying selection since the markers used for nucleotide divergences were not neutral, which can lead to a spurious ρ > 1. In conclusion, our study provided a statistical framework to test for diversifying selection acting on a quantitative trait while integrating the trove of genomic data available both within and between species, and we believe that our new approach is a promising tool to investigate the evolution of quantitative traits.
The activation status of macrophages was determined by MHC-II, CD80, and TNF expression. We observed no significant differences in frequency and cell count of activated CD80 + F4/80 + CD11b + macrophages and MHC-II + F4/80 + CD11b + macrophages between all groups of uninfected and CVB3-infected mice (data not shown). However, there was a significant reduction in TNF + F4/80 + CD11b + macrophage frequency in uninfected ΔHel1 (≈1. 6-fold, P < 0. 001) and KO (≈1. 3-fold, P < 0. 05) mice compared with uninfected NOD mice (Figure  3, A  and C ). Uninfected ΔHel1 mice also had a ≈1. 6-fold (P < 0. 05) decrease in cell count compared with uninfected NOD, but no differences were observed between uninfected NOD and KO mice (Figure  3, B  and C ). At day 7 postinfection, pancreatic TNF + F4/80 + CD11b + macrophage cell counts from CVB3-infected ΔHel1 mice were significantly reduced when compared with NOD (≈2.
JCI Insight 2023;8(2):e157929 https://doi. org/10. 1172/jci. insight. 157929 (Thermo Fisher Scientific) FoxP3 transcription factor fix/perm overnight. Cells were analyzed with an Attune NxT flow cytometer (Thermo Fisher Scientific) with ≈1,000,000 events/sample and analyzed with FlowJo version 10. 6. 2 software. The flow cytometry gating scheme is shown in Supplemental Figures  5  and 6. The average viability of pancreatic samples digested with collagenase was about 65%-80%, and any sample with less than 50% viability was excluded from analysis. BMDMs. BMDMs were generated from NOD, ΔHel1, and KO male mice at 8-12 weeks of age as previously described  (85). BMDMs were stimulated with 1 μg/mL of transfected HMW poly(I:C) using LyoVec (InvivoGen), 1 μg/mL LPS (E. coli 055:B5), or 100 MOI CVB3/Woodruff.
Western blot and Coomassie staining. MDA5, RIG-I, p-STAT1 (Y701), and STAT1 expression in untreated or poly(I:C)-, LPS-, or CVB3-stimulated BMDM whole-cell lysates was detected by Western blot analysis as previously described  (86). Purified proteins from size-exclusion chromatography were separated by SDS-PAGE, Coomassie stained GelCode Blue (Thermo Fisher Scientific), and then probed for MDA5 by Western blot analysis. Proteins were detected by incubation with primary antibodies (Supplemental Table  1B ) followed by an anti-rabbit IRDye 680/800CW secondary antibody (LI-COR Biosciences), visualized on an Odyssey CLx Imager with Image Studio v4. 0 software (LI-COR Biosciences) to calculate densitometry, and normalized to β-actin and unstimulated controls. Adoptive transfer of diabetes. Sixteen-week-old nondiabetic female NOD, ΔHel1, and KO spleens were resuspended at 10 8 cells/mL in HBSS.