Complex and diverse rupture processes of the 2018 Mw 8.2 and Mw 7.9 Tonga-Fiji deep earthquakes

Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 46(5), (2019):2434-2448, doi:10.1029/2018GL080997.Deep earthquakes exhibit strong variabilities in their rupture and aftershock characteristics, yet their physical failure mechanisms remain elusive. The 2018 Mw 8.2 and Mw 7.9 Tonga‐Fiji deep earthquakes, the two largest ever recorded in this subduction zone, occurred within days of each other. We investigate these events by performing waveform analysis, teleseismic P wave backprojection, and aftershock relocation. Our results show that the Mw 8.2 earthquake ruptured fast (4.1 km/s) and excited frequency‐dependent seismic radiation, whereas the Mw 7.9 earthquake ruptured slowly (2.5 km/s). Both events lasted ∼35 s. The Mw 8.2 earthquake initiated in the highly seismogenic, cold core of the slab and likely ruptured into the surrounding warmer materials, whereas the Mw 7.9 earthquake likely ruptured through a dissipative process in a previously aseismic region. The contrasts in earthquake kinematics and aftershock productivity argue for a combination of at least two primary mechanisms enabling rupture in the region.We thank the Editor Gavin Hayes and two anonymous reviewers for their helpful comments that improved the quality of the manuscript. The seismic data were provided by Data Management Center (DMC) of the Incorporated Research Institutions for Seismology (IRIS). The facilities of IRIS Data Services, and specifically the IRIS Data Management Center, were used for access to waveforms, related metadata, and/or derived products used in this study. IRIS Data Services are funded through the Seismological Facilities for the Advancement of Geoscience and EarthScope (SAGE) Proposal of the National Science Foundation under Cooperative Agreement EAR‐1261681. W. F. acknowledges supports from the Postdoctoral Scholar Program at the Woods Hole Oceanographic Institution, with funding provided by the Weston Howland Postdoctoral Scholarship. S. S. W. and D. T. are supported by the MSU Geological Sciences Endowment.2019-08-2

Seismological imaging of ridge–arc interaction beneath the Eastern Lau Spreading Center from OBS ambient noise tomography

The Lau Basin displays large along-strike variations in ridge characters with the changing proximity of the adjacent subduction zone. The mechanism governing these changes is not well understood but one hypotheses relates them to interaction between the arc and back-arc magmatic systems. We present a 3D seismic velocity model of the shallow mantle beneath the Eastern Lau back-arc Spreading Center (ELSC) and the adjacent Tofua volcanic arc obtained from ambient noise tomography of ocean bottom seismograph data. Our seismic images reveal an asymmetric upper mantle low velocity zone (LVZ) beneath the ELSC. Two major trends are present as the ridge-to-arc distance increases: (1) the LVZ becomes increasingly offset from the ridge to the north, where crust is thinner and the ridge less magmatically active; (2) the LVZ becomes increasingly connected to a sub-arc low velocity zone to the south. The separation of the ridge and arc low velocity zones is spatially coincident with the abrupt transition in crustal composition and ridge morphology. Our results present the first mantle imaging confirmation of a direct connection between crustal properties and uppermost mantle processes at ELSC, and support the prediction that as ELSC migrates away from the arc, a changing mantle wedge flow pattern leads to the separation of the arc and ridge melting regions. Slab-derived water is cutoff from the ridge, resulting in abrupt changes in crustal lava composition and crustal porosity. The larger offset between mantle melt supply and the ridge along the northern ELSC may reduce melt extraction efficiency along the ridge, further decreasing the melt budget and leading to the observed flat and faulted ridge morphology, thinner crust and the lack of an axial melt lens

p300/CBP-associated factor selectively regulates the extinction of conditioned fear

It is well established that the activity of chromatin-modifying enzymes is crucial for regulating gene expression associated with hippocampal-dependent memories. However, very little is known about how these epigenetic mechanisms influence the formation of cortically dependent memory, particularly when there is competition between opposing memory traces, such as that which occurs during the acquisition and extinction of conditioned fear. Here we demonstrate, in C57BL/6mice, that the activityofp300/CBP-associated factor (PCAF) within the infralimbic prefrontal cortex is required for long-term potentiation and is necessary for the formation of memory associated with fear extinction, but not for fear acquisition. Further, systemic administration of the PCAF activator SPV106 enhances memory for fear extinction and prevents fear renewal. The selective influence of PCAF on fear extinction is mediated, in part, by a transient recruitment of the repressive transcription factor ATF4tothe promoter of the immediate early genezif268, which competitively inhibits its expression. Thus, within the context of fear extinction, PCAF functions as a transcriptional coactivator, which may facilitate the formation of memory for fear extinction by interfering with reconsolidation of the original memory trace

A Comparison of Neural Decoding Methods and Population Coding Across Thalamo-Cortical Head Direction Cells

Head direction (HD) cells, which fire action potentials whenever an animal points its head in a particular direction, are thought to subserve the animal’s sense of spatial orientation. HD cells are found prominently in several thalamo-cortical regions including anterior thalamic nuclei, postsubiculum, medial entorhinal cortex, parasubiculum, and the parietal cortex. While a number of methods in neural decoding have been developed to assess the dynamics of spatial signals within thalamo-cortical regions, studies conducting a quantitative comparison of machine learning and statistical model-based decoding methods on HD cell activity are currently lacking. Here, we compare statistical model-based and machine learning approaches by assessing decoding accuracy and evaluate variables that contribute to population coding across thalamo-cortical HD cells

From fossils to mind

Fossil endocasts record features of brains from the past: size, shape, vasculature, and gyrification. These data, alongside experimental and comparative evidence, are needed to resolve questions about brain energetics, cognitive specializations, and developmental plasticity. Through the application of interdisciplinary techniques to the fossil record, paleoneurology has been leading major innovations. Neuroimaging is shedding light on fossil brain organization and behaviors. Inferences about the development and physiology of the brains of extinct species can be experimentally investigated through brain organoids and transgenic models based on ancient DNA. Phylogenetic comparative methods integrate data across species and associate genotypes to phenotypes, and brains to behaviors. Meanwhile, fossil and archeological discoveries continuously contribute new knowledge. Through cooperation, the scientific community can accelerate knowledge acquisition. Sharing digitized museum collections improves the availability of rare fossils and artifacts. Comparative neuroanatomical data are available through online databases, along with tools for their measurement and analysis. In the context of these advances, the paleoneurological record provides ample opportunity for future research. Biomedical and ecological sciences can benefit from paleoneurology's approach to understanding the mind as well as its novel research pipelines that establish connections between neuroanatomy, genes and behavior

A C-elegans stretch receptor neuron revealed by a mechanosensitive TRP channel homologue

The nematode Caenorhabditis elegans is commonly used as a genetic model organism for dissecting integration of the sensory and motor systems(1). Despite extensive genetic and behavioural analyses that have led to the identification of many genes and neural circuits involved in regulating C. elegans locomotion behaviour(1), it remains unclear whether and how somatosensory feedback modulates motor output during locomotion. In particular, no stretch receptors have been identified in C. elegans, raising the issue of whether stretch-receptor-mediated proprioception is used by C. elegans to regulate its locomotion behaviour. Here we have characterized TRP-4, the C. elegans homologue of the mechanosensitive TRPN channel. We show that trp-4 mutant worms bend their body abnormally, exhibiting a body posture distinct from that of wild-type worms during locomotion, suggesting that TRP-4 is involved in stretch-receptor-mediated proprioception. We show that TRP-4 acts in a single neuron, DVA, to mediate its function in proprioception, and that the activity of DVA can be stimulated by body stretch. DVA both positively and negatively modulates locomotion, providing a unique mechanism whereby a single neuron can fine-tune motor activity. Thus, DVA represents a stretch receptor neuron that regulates sensory - motor integration during C. elegans locomotion.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62770/1/nature04538.pd

Measurement of K-S(0) and K*(0) in p plus p, d plus Au, and Cu plus Cu collisions at root s(NN)=200 GeV

The PHENIX experiment at the Relativistic Heavy Ion Collider has performed a systematic study of K-S(0) and K*(0) meson production at midrapidity in p + p, d + Au, and Cu + Cu collisions at root s(NN) = 200 GeV. The K-S(0) and K*(0) mesons are reconstructed via their K-S(0) -\u3e pi(0)(-\u3e gamma gamma) pi(0)(-\u3e gamma gamma) and K*(0) -\u3e K-+/-pi(-/+) decay modes, respectively. The measured transverse-momentum spectra are used to determine the nuclear modification factor of K-S(0) and K*(0) mesons in d + Au and Cu + Cu collisions at different centralities. In the d + Au collisions, the nuclear modification factor of K-S(0) and K*(0) mesons is almost constant as a function of transverse momentum and is consistent with unity, showing that cold-nuclear-matter effects do not play a significant role in the measured kinematic range. In Cu + Cu collisions, within the uncertainties no nuclear modification is registered in peripheral collisions. In central collisions, both mesons show suppression relative to the expectations from the p + p yield scaled by the number of binary nucleon-nucleon collisions in the Cu + Cu system. In the p(T) range 2-5 GeV/c, the strange mesons (K-S(0), K*(0)) similarly to the phi meson with hidden strangeness, show an intermediate suppression between the more suppressed light quark mesons (pi(0)) and the nonsuppressed baryons (p, (p) over bar). At higher transverse momentum, p(T) \u3e 5 GeV/c, production of all particles is similarly suppressed by a factor of approximate to 2

ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders

Our approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, leverages a language-aligned image encoder combined or grafted onto a fixed LLM, PaLM 2, to perform a broad range of tasks. We train this lightweight adapter architecture using images paired with corresponding free-text radiology reports from the MIMIC-CXR dataset. ELIXR achieved state-of-the-art performance on zero-shot chest X-ray (CXR) classification (mean AUC of 0.850 across 13 findings), data-efficient CXR classification (mean AUCs of 0.893 and 0.898 across five findings (atelectasis, cardiomegaly, consolidation, pleural effusion, and pulmonary edema) for 1% (~2,200 images) and 10% (~22,000 images) training data), and semantic search (0.76 normalized discounted cumulative gain (NDCG) across nineteen queries, including perfect retrieval on twelve of them). Compared to existing data-efficient methods including supervised contrastive learning (SupCon), ELIXR required two orders of magnitude less data to reach similar performance. ELIXR also showed promise on CXR vision-language tasks, demonstrating overall accuracies of 58.7% and 62.5% on visual question answering and report quality assurance tasks, respectively. These results suggest that ELIXR is a robust and versatile approach to CXR AI

Direct evidence for cancer-cell-autonomous extracellular protein catabolism in pancreatic tumors

Mammalian tissues rely on a variety of nutrients to support their physiological functions. It is known that altered metabolism is involved in the pathogenesis of cancer, but which nutrients support the inappropriate growth of intact malignant tumors is incompletely understood. Amino acids are essential nutrients for many cancer cells that can be obtained through the scavenging and catabolism of extracellular protein via macropinocytosis. In particular, macropinocytosis can be a nutrient source for pancreatic cancer cells, but it is not fully understood how the tumor environment influences metabolic phenotypes and whether macropinocytosis supports the maintenance of amino acid levels within pancreatic tumors. Here we utilize miniaturized plasma exchange to deliver labeled albumin to tissues in live mice, and we demonstrate that breakdown of albumin contributes to the supply of free amino acids in pancreatic tumors. We also deliver albumin directly into tumors using an implantable microdevice, which was adapted and modified from ref. 9. Following implantation, we directly observe protein catabolism and macropinocytosis in situ by pancreatic cancer cells, but not by adjacent, non-cancerous pancreatic tissue. In addition, we find that intratumoral inhibition of macropinocytosis decreases amino acid levels. Taken together, these data suggest that pancreatic cancer cells consume extracellular protein, including albumin, and that this consumption serves as an important source of amino acids for pancreatic cancer cells in vivo.National Science Foundation (U.S.) (Grant T32GM007287)National Cancer Institute (U.S.) (Grant F30CA183474)National Institute of General Medical Sciences (U.S.) (Award T32GM007753)National Institutes of Health (U.S.) (Grant P30CA1405141)National Institutes of Health (U.S.) (Grant R01CA168653