Magnetotelluric and temperature monitoring after the 2011 sub-Plinian eruptions of Shinmoe-dake volcano

Three sub-Plinian eruptions took place on 26–27 January 2011 at Shinmoe-dake volcano in the Kirishima volcanic group, Japan. During this event, GPS and tiltmeters detected syn-eruptive ground subsidence approximately 7 km to the WNW of the volcano. Starting in March 2011, we conducted broad-band magnetotelluric (MT) measurements at a site located 5 km NNW of the volcano, beneath which the Shinmoe-dake magma plumbing system may exist. In addition, temperature monitoring of fumaroles and hot-springs near the MT site was initiated in July 2011. Our MT data record changes in apparent resistivity of approximately ±5%, along with a ±1◦ phase change in the off-diagonal component of the impedance tensor (Zxy and Zyx ). Using 1-D inversion, we infer that these slight changes in resistivity took place at relatively shallow depths of only a few hundred meters, at the transition between a near-surface resistive layer and an underlying conductive layer. Resistivity changes observed since March 2012 are correlated with the observed temperature increases around the MT monitoring site. These observations suggest the existence beneath the MT site of pathways which enable volatile escape

Geomagnetic pulsations caused by the Sumatra earthquake on December 26, 2004

A long period Pc5 pulsation was observed at Phimai in Thailand, shortly after the origin time of the Sumatra earthquake on December 26, 2004. The localized nature and the period of oscillations suggest that the long period magnetic pulsation was generated by dynamo action in the lower ionosphere, set up by an atmospheric pressure pulse which propagated vertically as an acoustic wave when the ocean floor suddenly moved vertically. It is speculated that a Pc3 type pulsation observed at Tong Hai in China, 10 degrees north of Phimai in latitude, was the result of magnetic field line resonance with a magnetosonic wave generated from the electric and magnetic fields of the dynamo current caused by the Earthquake.publishersversionPeer reviewe

Seismicity controlled by resistivity structure : the 2016 Kumamoto earthquakes, Kyushu Island, Japan

The M JMA 7.3 Kumamoto earthquake that occurred at 1:25 JST on April 16, 2016, not only triggered aftershocks in the vicinity of the epicenter, but also triggered earthquakes that were 50–100 km away from the epicenter of the main shock. The active seismicity can be divided into three regions: (1) the vicinity of the main faults, (2) the northern region of Aso volcano (50 km northeast of the mainshock epicenter), and (3) the regions around three volcanoes, Yufu, Tsurumi, and Garan (100 km northeast of the mainshock epicenter). Notably, the zones between these regions are distinctively seismically inactive. The electric resistivity structure estimated from one-dimensional analysis of the 247 broadband (0.005–3000 s) magnetotelluric and telluric observation sites clearly shows that the earthquakes occurred in resistive regions adjacent to conductive zones or resistive-conductive transition zones. In contrast, seismicity is quite low in electrically conductive zones, which are interpreted as regions of connected fluids. We suggest that the series of the earthquakes was induced by a local accumulated stress and/or fluid supply from conductive zones. Because the relationship between the earthquakes and the resistivity structure is consistent with previous studies, seismic hazard assessment generally can be improved by taking into account the resistivity structure. Following on from the 2016 Kumamoto earthquake series, we suggest that there are two zones that have a relatively high potential of earthquake generation along the western extension of the MTL

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

3-D inversion of magnetic data based on the L1-L2 norm regularization

Magnetic inversion is one of the popular methods to obtain information about the subsurface structure. However, many of the conventional methods have a serious problem, that is, the linear equations to be solved become ill-posed, under-determined, and thus, the uniqueness of the solution is not guaranteed. As a result, several different models fit the observed magnetic data with the same accuracy. To reduce the non-uniqueness of the model, conventional studies introduced regularization method based on the quadratic solution norm. However, these regularization methods impose a certain level of smoothness, and as the result, the resultant model is likely to be blurred. To obtain a focused magnetic model, I introduce L1 norm regularization. As is widely known, L1 norm regularization promotes sparseness of the model. So, it is expected that, the resulting model is constructed only with the features truly required to reconstruct data and, as a result, a simple and focused model is obtained. However, by using L1 norm regularization solely, an excessively concentrated model is obtained due to the nature of the L1 norm regularization and a lack of linear independence of the magnetic equations. To overcome this problem, I use a combination of L1 and L2 norm regularization. To choose a feasible regularization parameter, I introduce a regularization parameter selection method based on the L-curve criterion with fixing the mixing ratio of L1 and L2 norm regularization. This inversion method is applied to a real magnetic anomaly data observed on Hokkaido Island, northern Japan and reveals the subsurface magnetic structure on this area

Surface displacements of Aso volcano after the 2016 Kumamoto earthquake based on SAR interferometry: implications for dynamic triggering of earthquake-volcano interactions

The 2016 Kumamoto earthquake involved a series of events culminating in an Mw 7.0 main shock on 2016 April 16; the main-shock fault terminated in the caldera of Aso volcano. In this study, we estimated surface displacements after the 2016 Kumamoto earthquake using synthetic aperture radar interferometry analysis of 16 Phased Array Type L-band Synthetic Aperture Radar-2 images acquired from 2016 April 18 to 2017 June 12 and compared them with four images acquired before the earthquake. Ground subsidence of about 8 cm was observed within about a 3 km radius in the northwestern part of Aso caldera. Because this displacementwas not seen in data acquired before the 2016 Kumamoto earthquake, we attribute this displacement to the 2016 Kumamoto earthquake. Furthermore, to estimate the source depth of the surface displacement, we applied the Markov chain Monte Carlo method to a spherical source model and obtained a source depth of about 4.8 km. This depth and position are nearly in agreement with the top of a low-resistivity area previously inferred from magnetotelluric data; this area is thought to represent a deep hydrothermal reservoir. We concluded that this displacement is due to the migration of magma or aqueous fluids