Summary This study describes the epidemiology of a range of adult musculoskeletal soft tissue injuries. Our institution is the only hospital treating adults with musculoskeletal trauma in a ...well-defined catchment population of about 535,000. Demographic details over 5 years were recorded prospectively. Eighteen injury types were studied including anterior cruciate ligament (ACL) rupture, acromioclavicular joint (ACJ) injury, Achilles, patellar and quadriceps tendon ruptures, hand tendon injuries and mallet finger. 2794 patients presented with ligamentous or tedinous injuries over 5 years. 74.2% of patients were male, giving an incidence of 166.6/100,000 per year for males and 52.1/100,000 per year for females. The mean age was 36.3 years: 33.1 in males, 43.6 in females. 1040 (37.2%) were knee injuries: 75.6% were male with mean age 32.9, compared with 35.3 in females. 947 cases were hand tendon injuries (33.9%): 72.1% were male, with mean age 34.5 compared with 42.0 in females. Meniscal injury of the knee was the commonest injury with an incidence of 23.8/100,000 per year. Other common injuries were hand extensor tendons (18/100,000 per year), ACJ injury (14.5/100,000 per year), Achilles tendon rupture (11.3/100,000 per year), mallet finger (9.9/100,000 per year) and ACL rupture (8.1/100,000 per year). Achilles, patellar and quadriceps tendon rupture and mallet finger were injuries of middle age; rotator cuff tears and biceps tendon rupture were commoner in the elderly but all other injuries predominated in young patients. All injuries were commoner in males. Most soft tissue injuries follow distribution curves previously described for fracture epidemiology but three new distribution curves are presented for the injuries which predominate in middle age.
Seismicity along continental transform faults is usually confined to the upper half of the crust, but the Newport-1nglewood fault (NIF), a major fault traversing the Los Angeles basin, is seismically ...active down to the upper mantle. We use seismic array analysis to illuminate the seismogenic root of the NIF beneath Long Beach, California, and identify seismicity in an actively deforming localized zone penetrating the lithospheric mantle. Deep earthquakes, which are spatially correlated with geochemical evidence of a fluid pathway from the mantle, as well as with a sharp vertical offset in the lithosphere-asthenosphere boundary, exhibit narrow size distribution and weak temporal clustering. We attribute these characteristics to a transition from strong to weak interaction regimes in a system of seismic asperities embedded in a ductile fault zone matrix.
SUMMARY
New crustal images beneath Long Beach, California show the region of the Inner Borderland to continent transition. The cross-sections are obtained from stacked autocorrelations of virtual ...sources generated from oil-industry data recorded in the city of Long Beach, CA. They show that the Moho is dipping at 65° and obliquely truncates an ∼10 km thick flat-lying lower crustal fabric. The Moho appears to be fault controlled and an integral part of the extrusion of the Catalina Schist that underlays the Inner Borderland. The basement interface has significant offsets of up to 2 km, none of which correspond to the mapped trace of the Newport–Inglewood Fault.
We present a machine learning approach to classify the phases of surface wave dispersion curves. Standard frequency-time analysis (FTAN) analysis of seismograms observed on an array of receivers is ...converted into an image, of which each pixel is classified as fundamental mode, first overtone, or noise. We use a convolutional neural network (U-Net) architecture with a supervised learning objective and incorporate transfer learning. The training is initially performed with synthetic data to learn coarse structure, followed by fine-tuning of the network using approximately 10% of the real data based on human classification. The results show that the machine classification is nearly identical to the human picked phases. Expanding the method to process multiple images at once did not improve the performance. The developed technique will facilitate the automated processing of large dispersion curve data sets.
The role of osteoporosis and osteopenia in the etiology of fractures of the distal part of the radius is well established, but any link between osteoporosis and the severity of the distal radial ...fracture has not been extensively investigated. The aim of this study was to investigate the association between the degree of osteoporosis and the severity of distal radial fractures.
All patients over fifty-five years of age with a low-energy distal radial fracture were offered dual x-ray absorptiometry scanning of the hip. Data on the 137 consecutive patients were collected prospectively. Plain radiographs of the fractured distal part of the radius were assessed for angulation, metaphyseal and articular comminution, carpal malalignment, ulnar variance, AO/OTA group and subgroup classification, early and late displacement, and malunion. Fracture severity was quantified with use of previously published algorithms for calculating the probability of early and late displacement, late carpal malalignment, and malunion. These severity scores were correlated with the dual x-ray absorptiometry T-scores, which represent the number of standard deviations by which the measured bone density differs from the mean value in healthy controls.
There was a significant linear correlation between increasingly negative T-scores and increasing likelihood of early instability, late carpal malalignment, and malunion. Patients with osteoporosis (a T-score of less than -2.5) had a 43% probability of having early instability, a 39% probability of having late carpal malalignment, and a 66% probability of having malunion. Patients with osteopenia (a T-score of more than -2.5 but less than -1) had a 35% probability of having early instability, a 31% probability of having late carpal malalignment, and a 56% probability of having malunion. This compared with a 28% probability of early instability, a 25% probability of late carpal malalignment, and a 48% probability of malunion in patients with normal bone mineral density (a T-score of more than -1).
There is a definite correlation between bone mineral density and the severity of distal radial fractures.
Ambient seismic noise cross-correlations are now being used to detect temporal variations of seismic velocity, which are typically on the order of 0.1 per cent. At this small level, temporal ...variations in the properties of noise sources can cause apparent velocity changes. For example, the spatial distribution and frequency content of ambient noise have seasonal variations due to the seasonal hemispherical shift of storms. Here, we show that if the stretching method is used to measure time-shifts, then the temporal variability of noise frequency content causes apparent velocity changes due to the changes in both amplitude and phase spectra caused by waveform stretching. With realistic seasonal variations of frequency content in the Los Angeles Basin, our numerical tests produce about 0.05 per cent apparent velocity change, comparable to what Meier et al. observed in the Los Angeles Basin. We find that the apparent velocity change from waveform stretching depends on time windows and station-pair distances, and hence it is important to test a range of these parameters to diagnose the stretching bias. Better understanding of spatiotemporal noise source properties is critical for more accurate and reliable passive monitoring.
Seismic waveform modeling is a powerful tool for determining earth structure models and unraveling earthquake rupture processes, but it is usually computationally expensive. We introduce a scheme to ...vastly accelerate these calculations with a recently developed machine learning paradigm called the neural operator. Once trained, these models can simulate a full wavefield at negligible cost. We use a U-shaped neural operator to learn a general solution operator to the 2-D elastic wave equation from an ensemble of numerical simulations performed with random velocity models and source locations. We show that full-waveform modeling with neural operators is nearly two orders of magnitude faster than conventional numerical methods, and more importantly, the trained model enables accurate simulation for velocity models, source locations, and mesh discretization distinctly different from the training dataset. The method also enables convenient full-waveform inversion with automatic differentiation.
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