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Volcanic eruptions carry essential information on the dynamics of volcanic systems. Studies have documented variable eruption styles and eruptive surface deformation. However, co‐eruptive subsurface structural changes remain poorly understood. Here we characterize the seismic velocity changes from July 2019 to July 2023 at Great Sitkin Volcano in the central Aleutian volcanic arc, using single‐station ambient noise interferometry at five three‐component seismic stations. Coincident with the lava effusion since late July 2021, about two months after the explosive eruption on 26 May 2021, we observe a sustained velocity increase, most prominently to the northwest of the caldera. We attribute this velocity increase to the structural changes with magma extrusion, with the spatial variation controlled by the geometry of the magma system or the property of shallow volcaniclastics. Our findings offer insights into understanding co‐eruptive structural modifications at active volcanoes. Plain Language Summary: Volcanic eruptions provide important insights into how volcanoes work and the potential risks they pose. This study looks at the ongoing eruption at Great Sitkin Volcano in the central Aleutian volcanic arc to better understand the changes happening beneath the surface. We measure the changes in the velocity of seismic waves, with data from five seismometers on the island. We collected data from July 2019 to July 2023, covering different eruptive stages and multiple lava effusion episodes during the eruption that started on 26 May 2021. Coincident with the lava effusion since late July 2021, we notice a significant increase in seismic velocities, most prominently at a station northwest of the volcano's central crater. We conclude that this increase reflects the structural change in the shallow subsurface due to magma extrusion, with deflation of the edifice and closure of cracks. The findings in this study help to improve our understanding of how volcanoes behave during eruptions. Key Points: We observe prominent, spatially variable increases in seismic velocity during lava effusions at Great Sitkin Volcano since late July 2021The sustained co‐eruptive increase in seismic velocity reflects structural changes with crack closures as the consequence of magma extrusionThe distinct spatial variation of the velocity increase may be controlled by reservoir geometry or the strength of shallow volcaniclastics [ABSTRACT FROM AUTHOR]

Volcanic tremor is a semi‐continuous seismic and/or acoustic signal that occurs at time scales ranging from seconds to years, with variable amplitudes and spectral features. Tremor sources have often been related to fluid movement and degassing processes, and are recognized as a potential geophysical precursor and co‐eruptive geophysical signal. Eruption forecasting and monitoring efforts need a fast, robust method to automatically detect, characterize, and catalog volcanic tremor. Here we develop VOlcano Infrasound and Seismic Spectrogram Network (VOISS‐Net), a pair of convolutional neural networks (one for seismic, one for acoustic) that can detect tremor in near real‐time and classify it according to its spectral signature. Specifically, we construct an extensive data set of labeled seismic and low‐frequency acoustic (infrasound) spectrograms from the 2021–2022 eruption of Pavlof Volcano, Alaska, and use it to train VOISS‐Net to differentiate between different tremor types, explosions, earthquakes and noise. We use VOISS‐Net to classify continuous data from past Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022). VOISS‐Net achieves an 81.2% and 90.0% accuracy on the seismic and infrasound test sets respectively, and successfully characterizes tremor sequences for each eruption. By comparing the derived seismoacoustic timelines of each eruption with the corresponding eruption chronologies compiled by the Alaska Volcano Observatory, our model identifies changes in tremor regimes that coincide with observed volcanic activity. VOISS‐Net can aid tremor‐related monitoring and research by making consistent tremor catalogs more accessible. Plain Language Summary: Volcanic tremor is a persistent vibration of the ground, atmosphere, or both that can occur before and during volcanic eruptions. Despite its importance in volcano monitoring and eruption forecasting, volcano observatories do not have a reliable way of automatically detecting and identifying tremor due to the variable intensities and frequencies at which it occurs. In order to accomplish this, we develop and test a pair of machine learning models that classify spectrograms (i.e., images representing a signal's frequency content over time) from seismic and low‐frequency acoustic data. The models are trained on manually labeled images derived from the recent 2021–2022 eruption of Pavlof Volcano, Alaska, which demonstrated substantial signal diversity (e.g., different tremor types, earthquakes, explosions and noise). Our models achieve 81.2% and 90.0% accuracy on the seismic and low‐frequency acoustic test sets respectively, and perform well when applied to data recorded from past Pavlof Volcano eruptions. In addition, transitions in tremor sequences identified from our analysis generally coincide with shifts in eruptive patterns from Pavlof Volcano. Our tools can help volcano observatories systematically monitor tremor, and advance tremor research by making catalogs of their occurrences more consistent and accessible. Key Points: We develop a pair of convolutional neural networks that detect and classify volcano seismic and acoustic signalsWe apply our models to Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022) and derive volcano seismoacoustic timelinesThe seismoacoustic timelines reveal shifts in unrest regimes linked to explosions and effusive activity [ABSTRACT FROM AUTHOR]

Seismicity during explosive volcanic eruptions remains challenging to observe through the eruptive noise, leaving first‐order questions unanswered. How do earthquake rates change as eruptions progress, and what is their relationship to the opening and closing of the eruptive vent? To address these questions for the Okmok Volcano 2008 explosive eruption, Volcano Explosivity Index 4, we utilized modern detection methods to enhance the existing earthquake catalog. Our enhanced catalog detected significantly more earthquakes than traditional methods. We located, relocated, determined magnitudes and classified all events within this catalog. Our analysis reveals distinct behaviors for long‐period (LP) and volcano‐tectonic (VT) earthquakes, providing insights into the opening and closing cycle. LP earthquakes occur as bursts beneath the eruptive vent and do not coincide in time with the plumes, indicating their relationship to an eruptive process that occurs at a high pressurization state, that is, partially closed conduit. In contrast, VT earthquakes maintain a steadier rate over a broader region, do not track the caldera deflation and have a larger b‐value during the eruption than before or after. The closing sequence is marked by a burst of LPs followed by small VTs south of the volcano. The opening sequence differs as only VTs extend to depth and migrate within minutes of the eruption onset. Our high‐resolution catalog offers valuable insights, demonstrating that volcanic conduits can transition between partially closed (clogged) and open (cracked) states during an eruption. Utilizing modern earthquake processing techniques enables clearer understanding of eruptions and holds promise for studying other volcanic events. Plain Language Summary: Sparse instrumentation and high noise levels prevent us from seeing most of the quakes that occur. This problem is particularly severe during large volcanic eruptions when the explosive processes hide most of the seismic waves. Here, we overcome the problem by utilizing a suite of signal processing methods to dramatically increase our ability to see earthquakes during the noisiest, most difficult to monitor stage of a volcanic eruption. This high‐resolution data set unveils features of the volcanic structure as well as the dynamic interaction between the solid medium and the fluids. When the system is closed (clogged), the rocks are stressed by the trapped pressurized fluids and then break in earthquakes. When the system is open (cracked), ash and steam plumes grow higher, the rock is not being stressed as much and the number of observed earthquakes is lower around the eruptive vents. The volcano is displaying both open and closed vent behavior during a single eruption. Key Points: We analyzed the enhanced seismic catalog of a long‐lived explosive volcanic eruptive sequenceLong‐period earthquakes track eruptive processes when the system is partially closed and they are not associated with mass ejectionVolcano‐tectonic earthquakes are a secondary effect that does not track the deflation and occur at a low stress‐state [ABSTRACT FROM AUTHOR]

Volcanic seismicity provides essential insights into the behavior of an active volcano across multiple time scales. However, to understand how magma moves as the eruption cycle develops, better knowledge of the geometry and physical properties of the magma plumbing system is required. In this study, using full‐wave ambient noise tomography, we image the three‐dimensional (3‐D) crustal shear‐wave velocity structure below Great Sitkin Volcano in the central Aleutian Arc. The velocity model reveals two low‐velocity anomalies correlating with the migration of volcanic seismicity. With a bulk melt fraction of about 2.5%–9%, these low‐velocity anomalies are interpreted as mushy magma reservoirs. We propose a six‐stage eruption cycle to explain the migration of seismicity and the alternating eruption of the two reservoirs with different recharging histories. These findings have broad implications for the dynamics of magma plumbing systems and the structural control of eruption behaviors. Plain Language Summary: Understanding magma accumulation and transport systems below active volcanoes is essential for predicting eruption behavior and assessing the potential hazards. The distribution of earthquakes can partly be used to infer the development of magmatic activity at different times. However, to understand how magma moves at different stages of an eruption cycle, better knowledge of what the magma plumbing system looks like is necessary. In this study, we use an advanced seismic imaging method to construct the 3‐D crustal shear‐wave velocity structure below Great Sitkin Volcano in the central Aleutian Arc. The velocity model reveals two crustal magma reservoirs, which correlate with the migration of seismicity. We propose a six‐stage eruption cycle to explain the evolution of seismicity in space and time across the island and the alternating eruption of two reservoirs. The findings in this study help to understand better the control of eruption behaviors by the underlying magma plumbing system at active volcanoes. Key Points: The pre‐ and co‐eruptive seismicity below Great Sitkin Volcano, Alaska, shows a spatiotemporal migrationA new 3‐D shear‐wave velocity model reveals two crustal low‐velocity anomalies that correlate with the migrating seismicityWe propose a six‐stage eruption cycle involving two magma reservoirs to explain the long‐term and short‐term seismicity patterns [ABSTRACT FROM AUTHOR]

By providing unrivaled resolution in both time and space, volcano seismicity helps to chronicle and interpret eruptions. Standard earthquake detection methods are often insufficient as the eruption itself produces continuous seismic waves that obscure earthquake signals. We address this problem by developing an earthquake processing workflow specific to a high‐noise volcanic environment and applying it to the explosive 2008 Okmok Volcano eruption. This process includes applying single‐channel template matching combined with machine‐learning and fingerprint‐based techniques to expand the existing earthquake catalog of the eruption. We detected an order of magnitude more earthquakes, then located, relocated, determined locally calibrated magnitudes, and classified the events in the enhanced catalog. This new high‐resolution earthquake catalog increases the number of observations by about a factor of 10 and enables the detailed spatiotemporal seismic analysis during a large eruption. Plain Language Summary: Volcanic eruptions are some of the most challenging events to study seismically. The eruption itself continually excites seismic waves that hide the signatures of the accompanying earthquakes. The jetting of the plume, explosions, and impact of material on the ground all combine to create a blind spot in the seismological record. In this work, we develop a workflow that can overcome these limitations and reveal events that would otherwise remain hidden in the high‐noise, continuous seismic record. We utilize a combination of machine‐learning and related methods to increase the number of observed events by almost one order of magnitude. We determine the locations of the events using modern, high‐precision methods and use these locations with a customized, calibrated local magnitude scale for the region, which is necessary for volcanoes where seismic waves decay much faster than in the regions where standard magnitude scales were developed. We also automatically separate ordinary earthquakes from the more unusual long‐period ones and see an increase in detectability of both populations. The combined workflow now allows us to illuminate potentially the most interesting, and certainly most obscure, parts of the volcanic seismic cycle. Key Points: We develop a complete workflow suitable for co‐eruptive seismicity, which is inaccessible by normal seismic methodsWe combine different detection algorithms to increase the number of events of an explosive volcanic eruption by almost an order of magnitudeAutomated classification detects both long‐period and ordinary volcanic earthquakes [ABSTRACT FROM AUTHOR]

Discovery of novel drug delivery systems to the brain remains a key task for successful treatment of neurodegenerative disorders. Herein, the biodistribution of immunocyte‐based carriers, peripheral blood mononuclear cells (PBMCs), and monocyte‐derived EVs are investigated in adult rhesus macaques using longitudinal PET/MRI imaging. 64Cu‐labeled drug carriers are introduced via different routes of administration: intraperitoneal (IP), intravenous (IV), or intrathecal (IT) injection. Whole body PET/MRI (or PET/CT) images are acquired at 1, 24, and 48 h post injection of 64Cu‐labeled drug carriers, and standardized uptake values (SUVmean and SUVmax) in the main organs are estimated. The brain retention for both types of carriers increases based on route of administration: IP < IV < IT. Importantly, a single IT injection of PBMCs produces higher brain retention compared to IT injection of EVs. In contrast, EVs show superior brain accumulation compared to the cells when administered via IP and IV routes, respectively. Finally, a comprehensive chemistry panel of blood samples demonstrates no cytotoxic effects of either carrier. Overall, living cells and EVs have a great potential to be used for drug delivery to the brain. When identifying the ideal drug carrier, the route of administration could make big differences in CNS drug delivery. [ABSTRACT FROM AUTHOR]

The 2018 Kīlauea caldera collapse produced extraordinary sequences of seismicity and deformation, with 62 episodic collapse events which significantly altered the landscape of the summit region. Despite decades of focused scientific studies at Kīlauea, detailed information about the internal structure of the volcano is limited. Recently developed techniques in seismic interferometry can be used to monitor the internal structure of an active volcano more directly by detecting subtle spatiotemporal changes in seismic wave velocity, but their utility relies on accurate interpretations of the underlying phenomena causing those velocity changes. Here, we retrospectively apply repeating‐earthquake‐based seismic interferometry to the 2018 Kīlauea eruption sequence. We find that seismic velocities changed over two distinct time scales: a sudden increase followed by a slower decrease in velocity in the hours following each collapse event, and a gradual, long‐term decrease in velocity over several weeks that ceased approximately 1 month prior to the end of the eruption. Modeling suggests that short‐term changes can be explained by magma reservoir pressurization which specifically closed vertical ring fractures. Long‐term changes are related to subsidence of the caldera and likely include the influence of inelastic strain from the formation of new fractures. These observations provide new insights into the evolution of Kīlauea during its progressive collapse and will inform future interpretations for near‐real‐time monitoring at hazardous volcanoes around the world using similar techniques, especially where a dominant fracture orientation is present. Plain Language Summary: In 2018, the caldera of Kīlauea volcano, Hawaiʻi, collapsed as magma from the summit reservoir system drained to feed the eruption in the lower East Rift Zone. Changes in the area around the magma reservoir beneath Halemaʻumaʻu crater affected the speed of seismic wave propagation, which we measured using naturally occurring earthquakes. Changes in the seismic velocity were caused by the opening and closing of small cracks as the pressure in the reservoir changed and by the creation of a new fault. Our work shows that the orientation of cracks may be an important consideration when interpreting the relation between velocity change and deformation. Key Points: We used repeating earthquakes to derive relative seismic velocity changes with time during the last 2 months of caldera collapseSeismic velocity changes are correlated with deformation (strain) occurring over two distinct time scales with different causesFracture orientation may control which component of strain seismic velocity is sensitive to and important to consider for interpretation [ABSTRACT FROM AUTHOR]

Seismic waves are commonly used to monitor unrest before, during, and after volcanic eruptions. The source of seismic tremor during a sustained explosive volcanic eruption is not well understood. Recent observations of the 2016 eruption of Pavlof Volcano, Alaska, revealed a change in the relationship (hysteresis) between ash plume height and seismic amplitude over time. Based on similarities in physical processes and observed seismic tremor in rivers, we explore two key sources of seismic energy in the volcanic conduit: (1) forces exerted by particle impacts and (2) dynamic pressure changes by the turbulent flow. We develop a physical model calculating the seismic power spectral density (PSD), where forces on the conduit wall are convolved with the Green's function for Rayleigh waves. Using reasonable eruption parameters, the model is able to reproduce the frequency spectrum from the Pavlof eruption, although the modeled amplitudes are generally lower. We test the relative importance of different eruption parameters, including grain size, velocity, and conduit dimensions. We find that turbulence generally dominates over particle impacts. However, to reach the PSD amplitude during the Pavlof eruption, large grain sizes are required, as they have the greatest relative influence on the modeled amplitude. The hysteresis between plume height and seismic amplitude can then potentially be explained by grain size changes. The PSD shape is mostly determined by the Rayleigh‐wave quality factor Q, and substantial variations in seismic amplitude can be modeled assuming a constant mass eruption rate. Key Points: We develop a physical model for eruption tremor seismicity with turbulence and particle impacts as sourcesThe seismic power spectral density of the 2016 Pavlof Volcano eruption can be modeled with a realistic range of parameters and large grain sizesSensitivity analysis of parameters shows that grain size variations could cause the observed hysteresis in tremor‐plume height relationship [ABSTRACT FROM AUTHOR]

Conceptual models of magma storage and transport under calderas favor a connected system of sills and dikes. These features are individually below the resolution of standard seismic tomography, but radial seismic anisotropy can reveal where they exist in aggregate. We model radial anisotropy at Okmok caldera, Alaska, to demonstrate the presence of a caldera‐centered stacked sill complex and surrounding dike system. We show that ascending magma, inferred from seismicity, either intersects the sill complex, resulting in a larger volume eruption of evolved magma, or bypasses the overlying sill complex via dikes, resulting in a low‐volume mafic eruption. Our results exemplify how the locations of magma storage and paths of transport impact eruption size and composition. As this type of crustal storage is likely common to many calderas, this analysis offers a potential new framework for volcano observatories to forecast the size of impending eruptions. Plain Language Summary: Okmok caldera, in the central Aleutian Arc, has erupted approximately every decade for the last century and represents a notable hazard to the trans‐Pacific air traffic routes that overlie it. We deployed a temporary array of seismic instruments to supplement Okmok's existing seismic network. Modeling these seismic data, we identify a central region of magma storage at the volcano arranged in horizontal sheets (sills) surrounded by an adjacent ring of vertical sheets of cooled magma (dikes). Using the spatial patterns of earthquakes, we infer paths of magma transport in relation to these structures. For the 2008 Okmok eruption, we show that deeper, silica‐poor magma moved through and mobilized more silica‐rich magma stored in the shallow central sill region. This resulted in the 2008 eruption being larger and more explosive relative to the last century. In a typical historic eruption, by contrast, we conclude that the deeper magma source ascended through the dike system and bypassed the sill region, leading to smaller, silica‐poor eruptions. We suggest that the analysis presented in this study is a potential new framework for forecasting volcanic eruption size and may be of use to volcano observatories worldwide. Key Points: Okmok caldera is underlain by radially anisotropic velocity structures indicating a central sill complex and a surrounding ring of dikesThe path of magma through or around the central stacked sill complex can control the volume and composition of erupted magmas [ABSTRACT FROM AUTHOR]

The current deployment of the EarthScope Transportable Array (TA) in Alaska affords an unprecedented opportunity to study explosive volcanic eruptions using a relatively dense regional seismoacoustic network. Infrasound monitoring has demonstrated utility for the remote (>250 km range) detection and characterization of volcanic explosions, but previous studies have used relatively sparse regional or global networks. Seventy explosive events from the locally unmonitored Bogoslof volcano (2016–2017) provide a unique validation data set to examine the ability of the TA and other regional networks to detect and locate remote explosive volcanic eruptions in Alaska. With a simple envelope‐based reverse time migration (RTM) technique, we are able to detect and locate more than 72% of the 61 Bogoslof infrasound events detected by the Alaska Volcano Observatory. Notably, RTM using only sparse regional infrasound arrays produces results similar to when incorporating the extensive single‐sensor TA network, likely due to favorable signal‐to‐noise ratios, seasonal propagation conditions, and source‐receiver geometries. Our implementation also detects and locates explosive eruptions from Cleveland volcano, Alaska, and Bezymianny volcano, Kamchatka, as well as infrasound from nonvolcanic events such as earthquakes. We characterize the success of the RTM algorithm and associated parameter choices using receiver operating characteristic curves, event detection rates, and location accuracy. Our methods are useful for explosive volcanic and nonvolcanic event detection and localization using real‐time data and for scanning continuous waveform data archives. Key Points: Explosive eruptions from Bogoslof and Cleveland volcanoes are observed on the EarthScope Transportable Array, to more than 2,000 kmReverse time migration (RTM) backprojection approach detects and locates infrasound from volcanic and nonvolcanic eventsAdvantages and disadvantages are found for multiple RTM algorithms, each with variable detection rates and location accuracy [ABSTRACT FROM AUTHOR]

Ahyi seamount, a shallow submarine volcano in the Northern Mariana Islands, began erupting on 23 April 2014. Hydroacoustic eruption signals were observed on the regional Mariana seismic network and on distant hydrophones, and National Oceanic and Atmospheric Administration (NOAA) scuba divers working in the area soon after the eruption began heard and felt underwater explosion sounds. The NOAA crew observed yellow‐orange bubble mats along the shore of neighboring Farallon de Pájaros Island, but no other surface manifestations of the eruption were reported by the crew or observed in satellite data. Here, we detail the eruption chronology and its morphologic impacts through analysis of seismic and hydroacoustic recordings and repeat bathymetric mapping. Throughout the 2‐week‐long eruption, Ahyi produced several thousand short, impulsive hydroacoustic signals that we interpret as underwater explosions as well as tremor near the beginning and end of the sequence. The initial tremor, which occurred for 2 hr, is interpreted as small phreatomagmatic explosions. This tremor was followed by a 90‐min pause before the characteristic impulsive signals began. Occasional tremor (lasting up to a few minutes) during the last 1.5 days of the eruption is interpreted as more sustained eruptive activity. Bathymetric changes show that a new crater, about 150 m deep, formed near the former summit and a large landslide chute formed on the southeastern flank. Comparing to other geophysically detected submarine eruptions, we find that the signals from the 2014 Ahyi eruption were more similar to those from other shallow or at‐surface submarine eruptions than those at deep (>500 m) eruptions. Plain Language Summary: Ahyi seamount, a shallow submarine volcano in the Commonwealth of the Northern Mariana Islands (CNMI), began erupting on 23 April 2014. The U.S. Geological Survey first noticed signs of the eruption during a routine data check on 24 April, while National Oceanic and Atmospheric Administration scuba divers working in the area heard and felt underwater explosion sounds. We analyze recordings of the eruption on the CNMI seismic network and on hydrophones located near Wake Island to detail how the eruption unfolded. The eruption started with about 2 hr of tremor from magma explosively interacting with water. After a 90‐min pause, short (up to a few seconds) explosions began and continued for 2 weeks. During the last 1.5 days of the eruption, longer tremor signals (up to a few minutes) from more sustained degassing eruptions occurred along with the short explosions. A comparison of bathymetric maps made before and after the eruption shows that the explosions formed a new crater 150 m deep near the summit and that a landslide chute formed on the southeastern flank. The seismic and hydroacoustic signals from the Ahyi eruption are more similar to those from eruptions at other shallow or at‐surface seamounts than to those from deep (>500 m) eruptions. Key Points: The submarine volcano Ahyi erupted for 2 weeks in April–May 2014 and was recorded by regional seismometers and distant hydrophonesThe eruption was characterized by several thousand explosions and occasional tremor at the beginning and end of the eruptive periodRepeat bathymetry reveals a new summit crater and a new, large landslide chute on the south flank [ABSTRACT FROM AUTHOR]

Abstract: We present a new infrasonic signal detection and back azimuth determination technique that requires just one microphone and one three‐component seismometer. Ground‐coupled airwaves (GCAs) occur when an incident atmospheric acoustic wave impinges on the ground surface and is partially transmitted as a seismic wave. GCAs are commonly detected hundreds of kilometers away on seismic networks and are observed to have retrograde particle motion. Horizontally propagating acoustic waves and GCAs have previously been observed on collocated infrasound and seismic sensor pairs as coherent with a 90° phase difference. If the sensors are spatially separated, an additional propagation‐induced phase shift is present. The additional phase shift depends on the direction from which the acoustic wave arrives, as each back azimuth has a different apparent distance between the sensors. We use the additional phase shift, the coherence, and the characteristic particle motion on the three‐component seismometer to determine GCA arrivals and their unique back azimuth. We test this technique with synthetic seismo‐acoustic data generated by a coupled Earth‐atmosphere 3‐D finite difference code, as well as three seismo‐acoustic data sets from Mount St. Helens, Mount Cleveland, and Mount Pagan volcanoes. Results from our technique compare favorably with traditional infrasound array processing and provide robust GCA detection and back azimuth determination. Assuming adequate station spacing and sampling, our technique provides a new and robust method to detect infrasonic signals and determine their back azimuth, and may be of practical benefit where resources are limited and large sensor networks or arrays are not feasible. [ABSTRACT FROM AUTHOR]

Abstract: The two major explosive phases of the 22–23 April 2015 eruption of Calbuco volcano, Chile, produced powerful seismicity and infrasound. The eruption was recorded on seismo‐acoustic stations out to 1,540 km and on five stations (IS02, IS08, IS09, IS27, and IS49) of the International Monitoring System (IMS) infrasound network at distances from 1,525 to 5,122 km. The remote IMS infrasound stations provide an accurate explosion chronology consistent with the regional and local seismo‐acoustic data and with previous studies of lightning and plume observations. We use the IMS network to detect and locate the eruption signals using a brute‐force, grid‐search, cross‐bearings approach. After incorporating azimuth deviation corrections from stratospheric crosswinds using 3‐D ray tracing, the estimated source location is 172 km from true. This case study highlights the significant capability of the IMS infrasound network to provide automated detection, characterization, and timing estimates of global explosive volcanic activity. Augmenting the IMS with regional seismo‐acoustic networks will dramatically enhance volcanic signal detection, reduce latency, and improve discrimination capability. [ABSTRACT FROM AUTHOR]

Lightning often occurs during ash-producing eruptive activity, and its detection is now being used in volcano monitoring for rapid alerts. We report on infrasonic and sonic recordings of the related, but previously undocumented, phenomenon of volcanic thunder. We observe volcanic thunder during the waning stages of two explosive eruptions at Bogoslof volcano, Alaska, on a microphone array located 60 km away. Thunder signals arrive from a different direction than coeruptive infrasound generated at the vent following an eruption on 10 June 2017, consistent with locations from lightning networks. For the 8 March 2017 eruption, arrival times and amplitudes of high-frequency thunder signals correlate well with the timing and strength of lightning detections. In both cases, the thunder is associated with lightning that continues after significant eruptive activity has ended. Infrasonic and sonic observations of volcanic thunder offer a new avenue for studying electrification processes in volcanic plumes. [ABSTRACT FROM AUTHOR]

Individuals with psychosis have been reported to show either reduced or augmented brain responses under seemingly similar conditions. It is likely that inconsistent baseline-adjustment methods are partly responsible for this discrepancy. Using steady-state stimuli during a pro/antisaccade task, this study addressed the relationship between nonspecific and stimulus-related neural activity, and how these activities are modulated as a function of cognitive demands. In 98 psychosis probands (schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis), neural activity was assessed during baseline and during a 5-s period in preparation for the pro/antisaccade task. To maximize the ability to identify meaningful differences between psychosis subtypes, analyses were conducted as a function of subgrouping probands by standard clinical diagnoses and neurobiological features. These psychosis 'biotypes' were created using brain-based biomarkers, independent of symptomatology (Clementz et al., ). Psychosis probands as a whole showed poor antisaccade performance and diminished baseline oscillatory phase synchrony. Psychosis biotypes differed on both behavioral and brain measures, in ways predicted from Clementz et al. (). Two biotype groups showed similarly deficient behavior and baseline synchrony, despite diametrically opposed neural activity amplitudes. Another biotype subgroup was more similar to healthy individuals on behavioral and brain measures, despite the presence of psychosis. This study provides evidence that (a) consideration of baseline levels of activation and synchrony will be essential for a comprehensive understanding of neural response differences in psychosis, and (b) distinct psychosis subgroups exhibit reduced versus augmented intrinsic neural activity, despite cognitive performance and clinical similarities. [ABSTRACT FROM AUTHOR]

Delivery of nucleic acids into solid tumor environments remains a pressing challenge. This study examines the ability of macrophages to horizontally transfer small interfering RNA (siRNA) lipoplexes to cancer cells. Macrophages are a natural candidate for a drug carrier because of their ability to accumulate at high densities into many cancer types, including, breast, prostate, brain, and colon cancer. Here, it is demonstrated that macrophages can horizontally transfer siRNA to cancer cells during in vitro coculture. The amount of transfer can be dosed depending on the amount of siRNA loaded and total number of macrophages delivered. Macrophages loaded with calcium integrin binding protein‐1 (CIB1)‐siRNA result in decreased tumorsphere growth and decreased mRNA expression of CIB1 and KI67 in MDA‐MB‐468 human breast cancer cells. Adoptive transfer of macrophages transfected with CIB1‐siRNA localizes to the orthotopic MDA‐MB‐468 tumor. Furthermore, it is reported that macrophage activation can modulate this transfer process as well as intracellular trafficking protein Rab27a. As macrophages are heavily involved in tumor progression, understanding how to use macrophages for drug delivery can substantially benefit the treatment of tumors. [ABSTRACT FROM AUTHOR]