Evidence of Fluid Induced Earthquake Swarms From High Resolution Earthquake Relocation in the Main Ethiopian Rift

Fluid overpressure and fluid migration are known to be able to trigger or induce fault slip. However, relatively little is known about the role of fluids on generating earthquakes in some of the major continental rifts. To address this, we investigate the interaction between fluids and faults in the Main Ethiopian Rift (MER) using a large seismicity catalog that covers both the rift axis and rift margin. We performed cross-correlation analysis on four major earthquake clusters (three within the rift and one on the rift margin) in order to significantly improve accuracy of the earthquake relative relocations and to quantify families of earthquakes in which waveforms are similar. We also analyzed variation of seismicity rate and seismic moment release through time for the four clusters. The major results are that for all four clusters the earthquake relocations are 5–15 km deep, aligned to clear N-NNE striking, steeply (>60°) dipping planes. For the three clusters within the rift, the cross-correlation analysis identifies earthquake families that occur in short swarms during which seismic rate and moment release increases. Together, this space and time pattern of the seismicity strongly points toward them being fluid induced, with fluid likely sourced from depth such as mantle derived CO2. In contrast, the seismicity on the rift margin lacks earthquake families, with occurrence of earthquakes more continuous in nature, which we interpret as pointing toward tectonic stress-driven microseismic creep. Overall, our results suggest that deep sourced fluid migration within the rift is an important driver of earthquake activity

Single-Molecule Tracking of Collagenase on Native Type I Collagen Fibrils Reveals Degradation Mechanism

SummaryBackgroundCollagen, the most abundant human protein, is the principal component of the extracellular matrix and plays important roles in maintaining tissue and organ integrity. Highly resistant to proteolysis, fibrillar collagen is degraded by specific matrix metalloproteases (MMPs). Degradation of fibrillar collagen underlies processes including tissue remodeling, wound healing, and cancer metastasis. However, the mechanism of native collagen fibril degradation remains poorly understood.ResultsHere we present the results of high-resolution tracking of individual MMPs degrading type I collagen fibrils. MMP1 exhibits cleavage-dependent biased and hindered diffusion but spends 90% ± 3% of the time in one of at least two distinct pause states. One class of exponentially distributed pauses (class I pauses) occurs randomly along the fibril, whereas a second class of pauses (class II pauses) exhibits multistep escape kinetics and occurs periodically at intervals of 1.3 ± 0.2 μm and 1.5 ± 0.2 μm along the fibril. After these class II pauses, MMP1 moved faster and farther in one direction along the fibril, indicative of biased motion associated with cleavage. Simulations indicate that 5% ± 2% of the class II pauses result in the initiation of processive collagen degradation, which continues for bursts of 15 ± 4 consecutive cleavage events.ConclusionsThese findings provide a mechanistic paradigm for type I collagen degradation by MMP1 and establish a general approach to investigate MMP-fibrillar collagen interactions. More generally, this work demonstrates the fundamental role of enzyme-substrate interactions including binding and motion in determining the activity of an enzyme on an extended substrate

The Incidence of X-ray selected AGN in Nearby Galaxies

We present the identification and analysis of an unbiased sample of AGN that lie within the local galaxy population. Using the MPA-JHU catalogue (based on SDSS DR8) and 3XMM DR7 we define a parent sample of 25,949 local galaxies ($z \leq 0.33$). After confirming that there was strictly no AGN light contaminating stellar mass and star-formation rate calculations, we identified 917 galaxies with central, excess X-ray emission likely originating from an AGN. We analysed their optical emission lines using the BPT diagnostic and confirmed that such techniques are more effective at reliably identifying sources as AGN in higher mass galaxies: rising from 30% agreement in the lowest mass bin to 93% in the highest. We then calculated the growth rates of the black holes powering these AGN in terms of their specific accretion rates ($\propto L_X/M_*$). Our sample exhibits a wide range of accretion rates, with the majority accreting at rates $\leq 0.5\%$ of their Eddington luminosity. Finally, we used our sample to calculate the incidence of AGN as a function of stellar mass and redshift. After correcting for the varying sensitivity of 3XMM, we split the galaxy sample by stellar mass and redshift and investigated the AGN fraction as a function of X-ray luminosity and specific black hole accretion rate. From this we found the fraction of galaxies hosting AGN above a fixed specific accretion rate limit of $10^{-3.5}$ is constant (at $\approx 1\%$) over stellar masses of $8 < \log \mathrm{M_*/M_\odot} < 12$ and increases (from $\approx 1\%$ to $10\%$) with redshift.Comment: 18 pages, 10 figures, 2 appendices. Accepted for publication in MNRA

Disease Knowledge Transfer across Neurodegenerative Diseases

We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits biomarker relationships that are shared across diseases. Our proposed method allows, for the first time, the estimation of plausible, multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare neurodegenerative disease where only unimodal MRI data is available. For this we train DKT on a combined dataset containing subjects with two distinct diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD) dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for which only a limited number of Magnetic Resonance Imaging (MRI) scans are available. Although validation is challenging due to lack of data in PCA, we validate DKT on synthetic data and two patient datasets (TADPOLE and PCA cohorts), showing it can estimate the ground truth parameters in the simulation and predict unseen biomarkers on the two patient datasets. While we demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other forms of related neurodegenerative diseases. Source code for DKT is available online: https://github.com/mrazvan22/dkt.Comment: accepted at MICCAI 2019, 13 pages, 5 figures, 2 table