Understanding the connection between seismic activity and the earthquake nucleation process is a fundamental goal in earthquake seismology with important implications for earthquake early warning ...systems and forecasting. We use high-resolution acoustic emission (AE) waveform measurements from laboratory stick-slip experiments that span a spectrum of slow to fast slip rates to probe spatiotemporal properties of laboratory foreshocks and nucleation processes. We measure waveform similarity and pairwise differential travel-times (DTT) between AEs throughout the seismic cycle. AEs broadcasted prior to slow labquakes have small DTT and high waveform similarity relative to fast labquakes. We show that during slow stick-slip, the fault never fully locks, and waveform similarity and pairwise differential travel times do not evolve throughout the seismic cycle. In contrast, fast laboratory earthquakes are preceded by a rapid increase in waveform similarity late in the seismic cycle and a reduction in differential travel times, indicating that AEs begin to coalesce as the fault slip velocity increases leading up to failure. These observations point to key differences in the nucleation process of slow and fast labquakes and suggest that the spatiotemporal evolution of laboratory foreshocks is linked to fault slip velocity.
Nearly all aspects of earthquake rupture are controlled by the friction along the fault that progressively increases with tectonic forcing but in general cannot be directly measured. We show that ...fault friction can be determined at any time, from the continuous seismic signal. In a classic laboratory experiment of repeating earthquakes, we find that the seismic signal follows a specific pattern with respect to fault friction, allowing us to determine the fault's position within its failure cycle. Using machine learning, we show that instantaneous statistical characteristics of the seismic signal are a fingerprint of the fault zone shear stress and frictional state. Further analysis of this fingerprint leads to a simple equation of state quantitatively relating the seismic signal power and the friction on the fault. These results show that fault zone frictional characteristics and the state of stress in the surroundings of the fault can be inferred from seismic waves, at least in the laboratory.
Plain Language Summary
In a laboratory setting that closely mimics Earth faulting, we show that the most important physical properties of a fault can be accurately estimated using machine learning to analyze the sound that the fault broadcasts. The artificial intelligence identifies telltale sounds that are characteristic of the physical state of the fault, and how close it is to failing. A fundamental relation between the sound emitted by the fault and its physical state is thus revealed.
Key Points
Machine learning models can discern the frictional state of a laboratory fault from the statistical characteristics of the seismic signal
The use of machine learning uncovers an equation of state linking fault friction and statistical characteristics of the seismic signal
The discovery of this equation of state also uncovers the hysterectic behavior of the laboratory fault
OBJECTIVE:To explore the clinical and financial implications of preoperative opioid use in major abdominal surgery.
BACKGROUND:Opioids are increasingly used to manage chronic pain, and chronic opioid ...users are challenging to care for perioperatively. Given the epidemic of opioid-related morbidity and mortality, it is critical to understand how preoperative opioid use impacts surgical outcomes.
METHODS:This was an analysis of nonemergent, abdominopelvic surgeries from 2008 to 2014 from a single center within the Michigan Surgical Quality Collaborative clinical registry database. Preoperative opioid use (binary exposure variable) was retrospectively queried from the home medication list of the preoperative evaluation. Our primary outcome was 90-day total hospital costs. Secondary outcomes included hospital length of stay, 30-day major complication rates, discharge destination, and 30-day hospital readmission rates. Analyses were risk-adjusted for case complexity and patient-specific risk factors such as demographics, insurance, smoking, comorbidities, and concurrent medication use.
RESULTS:In all, 2413 patients met the inclusion criteria. Among them, 502 patients (21%) used opioids preoperatively. After covariate adjustment, opioid users (compared with those who were opioid-naïve) had 9.2% higher costs 95% confidence interval (CI) 2.8%–15.6%; adjusted means $26,604 vs $24,263; P = 0.005), 12.4% longer length of stay (95% CI 2.3%–23.5%; adjusted means 5.9 vs 5.2 days; P = 0.015), more complications (odds ratio 1.36; 95% CI 1.04–1.78; adjusted rates 20% vs 16%; P = 0.023), more readmissions (odds ratio 1.57; 95% CI 1.08–2.29; adjusted rates 10% vs 6%; P = 0.018), and no difference in discharge destination (P = 0.11).
CONCLUSIONS:Opioid use is common before abdominopelvic surgery, and is independently associated with increased postoperative healthcare utilization and morbidity. Preoperative opioids represent a potentially modifiable risk factor and a novel target to improve quality and value of surgical care.
We construct a fully self-consistent mass model for the lens galaxy SDSS J2141 at redshift 0.14, and use it to improve on previous studies by modelling its gravitational lensing effect, gas rotation ...curve and stellar kinematics simultaneously. We adopt a very flexible axisymmetric mass model constituted by a generalized Navarro-Frenk-White (NFW) dark matter halo and a stellar mass distribution obtained by deprojecting the multi-Gaussian expansion fit to the high-resolution K′-band laser guide star adaptive optics imaging data of the galaxy, with the (spatially constant) mass-to-light ratio as a free parameter. We model the stellar kinematics by solving the anisotropic Jeans equations. We find that the inner logarithmic slope of the dark halo is weakly constrained, i.e.
, and consistent with an unmodified NFW profile; we can conclude, however, that steep profiles (γ≥ 1.5) are disfavoured (<14 per cent posterior probability). We marginalize over this parameter to infer the galaxy to have (i) a dark matter fraction within 2.2 disc radii of
, independent of the galaxy stellar population, implying a maximal disc for SDSS J2141; (ii) an apparently uncontracted dark matter halo, with concentration
and virial velocity
, consistent with Λ cold dark matter (ΛCDM) predictions; (iii) a slightly oblate halo (
), consistent with predictions from baryon-affected models. Comparing the tightly constrained gravitational stellar mass inferred from the combined analysis (
) with that inferred from stellar population modelling of the galaxies' colours, and accounting for an expected cold gas fraction of 20 ± 10 per cent, we determine a preference for a Chabrier IMF over Salpeter IMF by a Bayes factor of 5.7 (corresponding to substantial evidence). We infer a value
for the orbital anisotropy parameter in the meridional plane, in agreement with most studies of local disc galaxies, and ruling out at 99 per cent confidence level that the dynamics of this system can be described by a two-integral distribution function.
Machine learning regression can predict macroscopic fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. Here we show that a ...similar approach is successful using event catalogs derived from the continuous data. Our methods are applicable to catalogs of arbitrary scale and magnitude of completeness. We investigate how machine learning regression from an event catalog of laboratory earthquakes performs as a function of the catalog magnitude of completeness. We find that strong model performance requires a sufficiently low magnitude of completeness, and below this magnitude of completeness, model performance saturates.
Plain Language Summary
Seismologists analyze faults in the earth by creating earthquake catalogs‐records of the times, locations, and sizes of earthquakes. For decades, researchers have attempted to use the these catalogs to predict the timing and size of future earthquakes. Recently, researchers have found that machine learning algorithms can forecast the motion of the fault using subtle “creaking” sounds, both in the laboratory and in the real world. These creaking sounds had previously been thought to be noise and were not commonly cataloged as earthquake activity. We installed a very powerful sensor in a laboratory fault and created a very detailed catalog that captures very small quakes—small enough that they would have looked like noise to a less powerful sensor. We then used machine learning on this catalog to try and forecast the large laboratory earthquakes. We found that machine learning model is successful when small‐enough events are part of the catalog. This says that subtle seismic sounds that look like noise may be very small earthquakes that were previously overlooked. These findings suggest that to improve earthquake forecasting, we might broaden our ideas of what signals to label as potential earthquakes and save in catalogs.
Key Points
Machine learning can model important characteristics of laboratory fault physics by training on finely resolved catalogs of slip events
Fault physics becomes significantly harder to learn if catalogs are truncated at or above a critical magnitude of completeness
Dyrk1A phosphorylated multiple proteins in the clathrin-coated vesicle (CCV) preparations obtained from rat brains. Mass spectrometric analysis identified MAP1A, MAP2, AP180, and α- and β-adaptins as ...the phosphorylated proteins in the CCVs. Each protein was subsequently confirmed by (32)P-labeling and immunological methods. The Dyrk1A-mediated phosphorylation released the majority of MAP1A and MAP2 and enhanced the release of AP180 and adaptin subunits from the CCVs. Furthermore, Dyrk1A displaced adaptor proteins physically from CCVs in a kinase-concentration dependent manner. The clathrin heavy chain release rate, in contrast, was not affected by Dyrk1A. Surprisingly, the Dyrk1A-mediated phosphorylation of α- and β-adaptins led to dissociation of the AP2 complex, and released only β-adaptin from the CCVs. AP180 was phosphorylated by Dyrk1A also in the membrane-free fractions, but α- and β-adaptins were not. Dyrk1A was detected in the isolated CCVs and was co-localized with clathrin in neurons from mouse brain sections and from primary cultured rat hippocampus. Previously, we proposed that Dyrk1A inhibits the onset of clathrin-mediated endocytosis in neurons by phosphorylating dynamin 1, amphiphysin 1, and synaptojanin 1. Current results suggest that besides the inhibition, Dyrk1A promotes the uncoating process of endocytosed CCVs.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Laboratory earthquake experiments provide important observational constraints for our understanding of earthquake physics. Here we leverage continuous waveform data from a network of piezoceramic ...sensors to study the spatial and temporal evolution of microslip activity during a shear experiment with synthetic fault gouge. We combine machine learning techniques with ray theoretical seismology to detect, associate, and locate tens of thousands of microslip events within the gouge layer. Microslip activity is concentrated near the center of the system but is highly variable in space and time. While microslip activity rate increases as failure approaches, the spatiotemporal evolution can differ substantially between stick‐slip cycles. These results illustrate that even within a single, well‐constrained laboratory experiment, the dynamics of earthquake nucleation can be highly complex.
Plain Language Summary
The fault systems that produce damaging earthquakes are difficult to study directly due to their depth and spatial extent in the Earth's crust. Laboratory earthquake experiments can provide insight into the relevant physical processes active in real earthquake systems. In experiments with granular material that emulates the crushed‐up gouge material of real faults, larger labquakes are always preceded by smaller, foreshock events. In this work, we provide a detailed study of the space‐time evolution of these microslip foreshocks in one such experiment. We show that even in these simplified analogs of real earthquake cycles, earthquake nucleation processes and frictional behavior can vary dramatically from cycle to cycle. In tectonic fault zones on Earth, such complexity will only be magnified.
Key Points
We study the spatiotemporal evolution of microslip events in laboratory earthquake experiments with synthetic granular fault gouge
We combine machine learning and conventional seismic processing techniques to develop a catalog of more than 30,000 microslip events
Microslip activity increases as failure approaches but exhibits a complex spatiotemporal pattern that varies throughout the experiment
Machine learning can predict the timing and magnitude of laboratory earthquakes using statistics of acoustic emissions. The evolution of acoustic energy is critical for lab earthquake prediction; ...however, the connections between acoustic energy and fault zone processes leading to failure are poorly understood. Here, we document in detail the temporal evolution of acoustic energy during the laboratory seismic cycle. We report on friction experiments for a range of shearing velocities, normal stresses, and granular particle sizes. Acoustic emission data are recorded continuously throughout shear using broadband piezo‐ceramic sensors. The coseismic acoustic energy release scales directly with stress drop and is consistent with concepts of frictional contact mechanics and time‐dependent fault healing. Experiments conducted with larger grains (10.5 μm) show that the temporal evolution of acoustic energy scales directly with fault slip rate. In particular, the acoustic energy is low when the fault is locked and increases to a maximum during coseismic failure. Data from traditional slide‐hold‐slide friction tests confirm that acoustic energy release is closely linked to fault slip rate. Furthermore, variations in the true contact area of fault zone particles play a key role in the generation of acoustic energy. Our data show that acoustic radiation is related primarily to breaking/sliding of frictional contact junctions, which suggests that machine learning‐based laboratory earthquake prediction derives from frictional weakening processes that begin very early in the seismic cycle and well before macroscopic failure.
Key Points
Coseismic energy release during laboratory earthquakes scales directly with stress drop
Acoustic energy radiated throughout the lab seismic cycle tacks fault slip rate and depends on contact junction size
Acoustic energy from laboratory foreshocks and mainshocks derives from breaking and sliding of frictional contact junctions
The relative contribution of baryons and dark matter to the inner regions of spiral galaxies provides critical clues to their formation and evolution, but it is generally difficult to determine. For ...spiral galaxies that are strong gravitational lenses, however, the combination of lensing and kinematic observations can be used to break the disc-halo degeneracy. In turn, such data constrain fundamental parameters such as (i) the mass density profile slope and axial ratio of the dark matter halo, and by comparison with dark matter-only numerical simulations the modifications imposed by baryons; (ii) the mass in stars and therefore the overall star formation efficiency, and the amount of feedback; (iii) by comparison with stellar population synthesis models, the normalization of the stellar initial mass function. In this first paper of a series, we present a sample of 16 secure, one probable and six possible strong lensing spiral galaxies, for which multiband high-resolution images and rotation curves were obtained using the Hubble Space Telescope and Keck II telescope as part of the Sloan WFC Edge-on Late-type Lens Survey (SWELLS). The sample includes eight newly discovered secure systems. We characterize the sample of deflector galaxies in terms of their morphologies, structural parameters and stellar masses. We find that the SWELLS sample of secure lenses spans a broad range of morphologies (from lenticular to late-type spiral), spectral types (quantified by Hα emission) and bulge to total stellar mass ratio (0.22-0.85), while being limited to M
* > 1010.5 M⊙. The SWELLS sample is thus well suited for exploring the relationship between dark and luminous matter in a broad range of galaxies. We find that the deflector galaxies obey the same size-mass relation as that of a comparison sample of elongated non-lens galaxies selected from the Sloan Digital Sky Survey. We conclude that the SWELLS sample is consistent with being representative of the overall population of high-mass high-inclination discy galaxies.
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