Spatio-temporal Scanning and Statistical Test of the Accelerating Moment Release (AMR) Model Using Australian Earthquake Data

Abstract. For revealing the preparatory processes of large inland earthquakes, we systematically applied the Pattern Informatics method (PI method) to the earthquake data of Japan. We focused on 12 large earthquakes with magnitudes larger than M = 6.4 (an official magnitude of the Japan Meteorological Agency) that occurred at depths shallower than 30 km between 2000 and 2010. We examined the relation between the spatiotemporal locations of such large shallow earthquakes and those of PI hotspots, which correspond to the grid cells of anomalous seismic activities in a designated time span. Based on a statistical test using Molchan's error diagram, we inquired into the existence of precursory anomalous seismic activities of the large earthquakes and, if any, their characteristic time span. The test indicated that the Japanese M &geqq; 6.4 inland earthquakes tend to be preceded by anomalous seismic activities of 8-to-10-yr time scales.

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Geovisual Analytics of Spatio-Temporal Earthquake Data in Indonesia

JGISE Journal of Geospatial Information Science and Engineering ◽

10.22146/jgise.51131 ◽

2019 ◽

Vol 2 (2) ◽

Author(s):

Febrian Fitryanik Susanta ◽

Cecep Pratama ◽

Trias Aditya ◽

Alian Fathira Khomaini ◽

Hadi Wijaya Kusuma Abdillah

Keyword(s):

Time Series ◽

Spatial Data ◽

Visual Analytics ◽

The Public ◽

Coordinate Position ◽

Geovisual Analytics ◽

History Of ◽

Spatio Temporal ◽

High Level ◽

Earthquake Data

Indonesia is one of the nations located in the Ring of Fire. Indonesia has a high level of geodynamic activities so that it's often earthquake tectonics. The earthquakes are caused by Indonesia position located in the confluence of four main plates. At present, the history of earthquake data in Indonesia has been accessible by the public. However, general visualization which can present history earthquake in the form maps and summary statistics have not been available. Therefore, this research aims to visualize the history of earthquake data interactively combining spatial data and temporal data. The data used for this research was obtained from BMKG website. The data variables used in this research include CORS stations and history of earthquake phenomenons between 2004 and 2019. The earthquake phenomenon consists of occurrence time, coordinate position, depth and magnitude. The data are processed using Ms Excel and ArcGIS Online Map then are visualized by Web AppBuilder for ArcGIS. The results of the data processing are maps presented in a dashboard with time-series animation and widgets features. We performed maps, graphics and time-series animation as interactive visual interfaces and matched the tasks to visual analytics techniques that are capable to support them. In this paper, we introduce the relationship between variables and present the visual analytics techniques using several example scenarios of Spatio-temporal earthquake data.

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Spatio-Temporal Associative Mining for Earthquake Data Distribution in Indonesia

EMITTER International Journal of Engineering Technology ◽

10.24003/emitter.v7i2.428 ◽

2019 ◽

Vol 7 (2) ◽

Author(s):

Renovita Edelani ◽

Aliridho Barakbah ◽

Tri Harsono

Keyword(s):

Data Distribution ◽

North Sulawesi ◽

Mapping System ◽

Earthquake Frequency ◽

West Papua ◽

Temporal Model ◽

Fire Area ◽

Spatio Temporal ◽

Confidence Value ◽

Earthquake Data

Indonesia is a country that has the highest seismically activity in the world. This country has really high earthquake frequency because of it traversed by three plate meeting plate and located in Ring of Fire area. The shaking events from an earthquake are very strong and propagate in all directions, capable of destroying even the strongest civilian buildings, so there is no doubt that there are many victims of human lives. The other facts, earthquake in Indonesia have seismic relation between the provinces. In this paper, we present a new earthquake Spatio-temporal mapping system based on the association confidence value from the result of associative mining process on earthquake data distribution in Indonesia. The system proposed three main functions which are (1) Data Acquisition which taken from four data provider, then preprocess and combine it become one, (2) Associative Mining process to get the rule of association earthquake between provinces in Indonesia, and (3) Earthquake Association Spatio-Temporal Model from the highest confidence value and Visualization. We use data from several earthquake data providers from 1900 until 2018. To perform our proposed Spatio-temporal earthquake association mapping system, we divided the data to become a 5-year discrete partition. After that, we mining the rule and get the highest confidence value from each period. This confidence value is used for modeling and visualization of our Spatio-temporal mapping system. As a result of this study, we manage to generate earthquake association risk mapping from 13 provinces that had earthquake connectivity between each other. The provinces are Aceh, Sumatera Utara, Bengkulu, East Java, Bali, NTB, NTT, Maluku, North Maluku, Gorontalo, North Sulawesi, Papua dan West Papua.

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E3 ubiquitin ligases

Essays in Biochemistry ◽

10.1042/bse0410015 ◽

2005 ◽

Vol 41 ◽

pp. 15-30 ◽

Cited By ~ 123

Author(s):

Helen C. Ardley ◽

Philip A. Robinson

Keyword(s):

Protein Complexes ◽

Loss Of Function ◽

Direct Role ◽

Cellular Processes ◽

Substrate Protein ◽

Protein Ubiquitination ◽

C Terminus ◽

Full Complement ◽

Spatio Temporal ◽

Eukaryotic Organisms

The selectivity of the ubiquitin–26 S proteasome system (UPS) for a particular substrate protein relies on the interaction between a ubiquitin-conjugating enzyme (E2, of which a cell contains relatively few) and a ubiquitin–protein ligase (E3, of which there are possibly hundreds). Post-translational modifications of the protein substrate, such as phosphorylation or hydroxylation, are often required prior to its selection. In this way, the precise spatio-temporal targeting and degradation of a given substrate can be achieved. The E3s are a large, diverse group of proteins, characterized by one of several defining motifs. These include a HECT (homologous to E6-associated protein C-terminus), RING (really interesting new gene) or U-box (a modified RING motif without the full complement of Zn2+-binding ligands) domain. Whereas HECT E3s have a direct role in catalysis during ubiquitination, RING and U-box E3s facilitate protein ubiquitination. These latter two E3 types act as adaptor-like molecules. They bring an E2 and a substrate into sufficiently close proximity to promote the substrate's ubiquitination. Although many RING-type E3s, such as MDM2 (murine double minute clone 2 oncoprotein) and c-Cbl, can apparently act alone, others are found as components of much larger multi-protein complexes, such as the anaphase-promoting complex. Taken together, these multifaceted properties and interactions enable E3s to provide a powerful, and specific, mechanism for protein clearance within all cells of eukaryotic organisms. The importance of E3s is highlighted by the number of normal cellular processes they regulate, and the number of diseases associated with their loss of function or inappropriate targeting.

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Elucidating cyclic AMP signaling in subcellular domains with optogenetic tools and fluorescent biosensors

Biochemical Society Transactions ◽

10.1042/bst20190246 ◽

2019 ◽

Vol 47 (6) ◽

pp. 1733-1747 ◽

Cited By ~ 3

Author(s):

Christina Klausen ◽

Fabian Kaiser ◽

Birthe Stüven ◽

Jan N. Hansen ◽

Dagmar Wachten

Keyword(s):

Cyclic Amp ◽

Adenosine Monophosphate ◽

Adenylyl Cyclases ◽

Temporal Precision ◽

Fluorescent Biosensors ◽

Subcellular Domains ◽

Spatio Temporal ◽

Hydrolysis Of ◽

Shed Light ◽

Cyclic Amp Signaling

The second messenger 3′,5′-cyclic nucleoside adenosine monophosphate (cAMP) plays a key role in signal transduction across prokaryotes and eukaryotes. Cyclic AMP signaling is compartmentalized into microdomains to fulfil specific functions. To define the function of cAMP within these microdomains, signaling needs to be analyzed with spatio-temporal precision. To this end, optogenetic approaches and genetically encoded fluorescent biosensors are particularly well suited. Synthesis and hydrolysis of cAMP can be directly manipulated by photoactivated adenylyl cyclases (PACs) and light-regulated phosphodiesterases (PDEs), respectively. In addition, many biosensors have been designed to spatially and temporarily resolve cAMP dynamics in the cell. This review provides an overview about optogenetic tools and biosensors to shed light on the subcellular organization of cAMP signaling.

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