Dynamic removal of replication protein A by Dna2 facilitates primer cleavage during Okazaki fragment processing in Saccharomyces cerevisiae

Eukaryotic Okazaki fragments are initiated by an RNA/DNA primer, which is removed before the fragments are joined. Polymerase d displaces the primer into a flap for processing. Dna2 nuclease/helicase and flap endonuclease 1 (FEN1) are proposed to cleave the flap. The single-stranded DNA binding protein, replication protein A (RPA), governs cleavage activity. Flap-bound RPA inhibits FEN1. This necessitates cleavage by Dna2, which is stimulated by RPA. FEN1 then cuts the remaining RPA-free flap to create a nick for ligation. Cleavage by Dna2 requires that it enter the 5'-end and track down the flap. Since Dna2 cleaves the RPA-bound flap, we investigated the mechanism by which Dna2 accesses the protein-coated flap for cleavage. Using a nuclease-defective Dna2 mutant, we showed that just binding of Dna2 dissociates the flap-bound RPA. Facile dissociation is specific to substrates with a genuine flap, and will not occur with an RPA-coated single strand. We also compared the cleavage patterns of Dna2 with and without RPA to better define RPA stimulation of Dna2. Stimulation derived from removal of DNA folding in the flap. Apparently, coordinated with its dissociation, RPA relinquishes the flap to Dna2 for tracking in a way that does not allow flap structure to reform. We also found that RPA strand melting activity promotes excessive flap elongation, but it is suppressed by Dna2-promoted RPA dissociation. Overall, results indicate that Dna2 and RPA coordinate their functions for efficient flap cleavage and preparation for FEN1

Probing the distance and morphology of the Large Magellanic Cloud with RR Lyrae stars

We present a Bayesian analysis of the distances to 15,040 Large Magellanic Cloud (LMC) RR Lyrae stars using $V$- and $I$-band light curves from the Optical Gravitational Lensing Experiment, in combination with new $z$-band observations from the Dark Energy Camera. Our median individual RR Lyrae distance statistical error is 1.89 kpc (fractional distance error of 3.76 per cent). We present three-dimensional contour plots of the number density of LMC RR Lyrae stars and measure a distance to the core LMC RR Lyrae centre of ${50.2482\pm0.0546 {\rm(statistical)} \pm0.4628 {\rm(systematic)} {\rm kpc}}$, equivalently ${\mu_{\rm LMC}=18.5056\pm0.0024 {\rm(statistical)} \pm0.02 {\rm(systematic)}}$. This finding is statistically consistent with and four times more precise than the canonical value determined by a recent meta-analysis of 233 separate LMC distance determinations. We also measure a maximum tilt angle of $11.84^{\circ}\pm0.80^{\circ}$ at a position angle of $62^\circ$, and report highly precise constraints on the $V$, $I$, and $z$ RR Lyrae period--magnitude relations. The full dataset of observed mean-flux magnitudes, derived colour excess ${E(V-I)}$ values, and fitted distances for the 15,040 RR Lyrae stars produced through this work is made available through the publication's associated online data.Comment: 7 pages, 8 figure

Meteorology of Jupiter's Equatorial Hot Spots and Plumes from Cassini

We present an updated analysis of Jupiter's equatorial meteorology from Cassini observations. For two months preceding the spacecraft's closest approach, the Imaging Science Subsystem (ISS) onboard regularly imaged the atmosphere. We created time-lapse movies from this period in order to analyze the dynamics of equatorial hot spots and their interactions with adjacent latitudes. Hot spots are quasi-stable, rectangular dark areas on visible-wavelength images, with defined eastern edges that sharply contrast with surrounding clouds, but diffuse western edges serving as nebulous boundaries with adjacent equatorial plumes. Hot spots exhibit significant variations in size and shape over timescales of days and weeks. Some of these changes correspond with passing vortex systems from adjacent latitudes interacting with hot spots. Strong anticyclonic gyres present to the south and southeast of the dark areas appear to circulate into hot spots. Impressive, bright white plumes occupy spaces in between hot spots. Compact cirrus-like 'scooter' clouds flow rapidly through the plumes before disappearing within the dark areas. These clouds travel at 150-200 m/s, much faster than the 100 m/s hot spot and plume drift speed. This raises the possibility that the scooter clouds may be more illustrative of the actual jet stream speed at these latitudes. Most previously published zonal wind profiles represent the drift speed of the hot spots at their latitude from pattern matching of the entire longitudinal image strip. If a downward branch of an equatorially-trapped Rossby waves controls the overall appearance of hot spots, however, the westward phase velocity of the wave leads to underestimates of the true jet stream speed.Comment: 33 pages, 11 figures; accepted for publication in Icarus; for supplementary movies, please contact autho

Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process

We present an active learning algorithm for learning dynamics that leverages side information by explicitly incorporating prior domain knowledge into the sampling process. Our proposed algorithm guides the exploration toward regions that demonstrate high empirical discrepancy between the observed data and an imperfect prior model of the dynamics derived from side information. Through numerical experiments, we demonstrate that this strategy explores regions of high discrepancy and accelerates learning while simultaneously reducing model uncertainty. We rigorously prove that our active learning algorithm yields a consistent estimate of the underlying dynamics by providing an explicit rate of convergence for the maximum predictive variance. We demonstrate the efficacy of our approach on an under-actuated pendulum system and on the half-cheetah MuJoCo environment

Peripheral Innate Immune Activation Correlates With Disease Severity in GRN Haploinsufficiency.

Objective: To investigate associations between peripheral innate immune activation and frontotemporal lobar degeneration (FTLD) in progranulin gene (GRN) haploinsufficiency. Methods: In this cross-sectional study, ELISA was used to measure six markers of innate immunity (sCD163, CCL18, LBP, sCD14, IL-18, and CRP) in plasma from 30 GRN mutation carriers (17 asymptomatic, 13 symptomatic) and 29 controls. Voxel based morphometry was used to model associations between marker levels and brain atrophy in mutation carriers relative to controls. Linear regression was used to model relationships between plasma marker levels with mean frontal white matter integrity [fractional anisotropy (FA)] and the FTLD modified Clinical Dementia Rating Scale sum of boxes score (FTLD-CDR SB). Results: Plasma sCD163 was higher in symptomatic GRN carriers [mean 321 ng/ml (SD 125)] compared to controls [mean 248 ng/ml (SD 58); p < 0.05]. Plasma CCL18 was higher in symptomatic GRN carriers [mean 56.9 pg/ml (SD 19)] compared to controls [mean 40.5 pg/ml (SD 14); p < 0.05]. Elevation of plasma LBP was associated with white matter atrophy in the right frontal pole and left inferior frontal gyrus (p FWE corrected <0.05) in all mutation carriers relative to controls. Plasma LBP levels inversely correlated with bilateral frontal white matter FA (R2 = 0.59, p = 0.009) in mutation carriers. Elevation in plasma was positively correlated with CDR-FTLD SB (b = 2.27 CDR units/μg LBP/ml plasma, R2 = 0.76, p = 0.003) in symptomatic carriers. Conclusion: FTLD-GRN is associated with elevations in peripheral biomarkers of macrophage-mediated innate immunity, including sCD163 and CCL18. Clinical disease severity and white matter integrity are correlated with blood LBP, suggesting a role for peripheral immune activation in FTLD-GRN

Random boundaries: quantifying segmentation uncertainty in solutions to boundary-value problems

Engineering simulations using boundary-value partial differential equations often implicitly assume that the uncertainty in the location of the boundary has a negligible impact on the output of the simulation. In this work, we develop a novel method for describing the geometric uncertainty in image-derived models and use a naive method for subsequently quantifying a simulation's sensitivity to that uncertainty. A Gaussian random field is constructed to represent the space of possible geometries, based on image-derived quantities such as pixel size, which can then be used to probe the simulation's output space. The algorithm is demonstrated with examples from biomechanics where patient-specific geometries are often segmented from low-resolution, three-dimensional images. These examples show the method's wide applicability with examples using linear elasticity and fluid dynamics. We show that important biomechanical outputs of these example simulations, namely maximum principal stress and wall shear stress, can be highly sensitive to realistic uncertainties in geometry