Greedy Bastards

Senior Project submitted to The Division of Social Studies of Bard College

Using dense seismo-acoustic network to provide timely warning of the 2019 paroxysmal Stromboli eruptions

Stromboli Volcano is well known for its persistent explosive activity. On July 3rd and August 28th 2019, two paroxysmal explosions occurred, generating an eruptive column that quickly rose up to 5 km above sea level. Both events were detected by advanced local monitoring networks operated by Istituto Nazionale di Geofisica e Vulcanologia (INGV) and Laboratorio di Geofisica Sperimentale of the University of Firenze (LGS-UNIFI). Signals were also recorded by the Italian national seismic network at a range of hundreds of kilometres and by infrasonic arrays up to distances of 3700 km. Using state-of-the-art propagation modeling, we identify the various seismic and infrasound phases that are used for precise timing of the eruptions. We highlight the advantage of dense regional seismo-acoustic networks to enhance volcanic signal detection in poorly monitored regions, to provide timely warning of eruptions and reliable source amplitude estimate to Volcanic Ash Advisory Centres (VAAC)

Local, Regional, and Remote Seismo‐Acoustic Observations of the April 2015 VEI 4 Eruption of Calbuco Volcano, Chile

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

Parallel microarray profiling identifies ErbB4 as a determinant of cyst growth in ADPKD and a prognostic biomarker for disease progression.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the fourth most common cause of end-stage renal disease. The disease course can be highly variable and treatment options are limited. To identify new therapeutic targets and prognostic biomarkers of disease, we conducted parallel discovery microarray profiling in normal and diseased human PKD1 cystic kidney cells. A total of 1515 genes and 5 miRNA were differentially expressed by more than two-fold in PKD1 cells. Functional enrichment analysis identified 30 dysregulated signalling pathways including the epidermal growth factor (EGF) receptor pathway. In this paper, we report that the EGF family receptor, ErbB4, is a major factor driving cyst growth in ADPKD. Expression of ErbB4 in vivo was increased in human ADPKD and Pkd1 cystic kidneys, both transcriptionally and post-transcriptionally by mir-193b-3p. Ligand-induced activation of ErbB4 drives cystic proliferation and expansion suggesting a pathogenic role in cystogenesis. Our results implicate ErbB4 activation as functionally relevant in ADPKD, both as a marker of disease activity and as a new therapeutic target in this major kidney disease

Modulatory Communication Signal Performance Is Associated with a Distinct Neurogenomic State in Honey Bees

Studies of animal communication systems have revealed that the perception of a salient signal can cause large-scale changes in brain gene expression, but little is known about how communication affects the neurogenomic state of the sender. We explored this issue by studying honey bees that produce a vibratory modulatory signal. We chose this system because it represents an extreme case of animal communication; some bees perform this behavior intensively, effectively acting as communication specialists. We show large differences in patterns of brain gene expression between individuals producing vibratory signal as compared with carefully matched non-senders. Some of the differentially regulated genes have previously been implicated in the performance of other motor activities, including courtship behavior in Drosophila melanogaster and Parkinson's Disease in humans. Our results demonstrate for the first time a neurogenomic brain state associated with sending a communication signal and provide suggestive glimpses of molecular roots for motor control