Spectral Attenuation Characteristics of Strong Ground Motions in East-Central Iran Using Theoretical Data

Ground-motion prediction equations are an essential element of PSHA. In seismic hazard analysis, attenuation calculations determine how quickly ground motions decrease as the distance from a seismic event increases. The estimation of ground motion for future earthquakes as a function of magnitude and distance is an important problem from earthquake engineering point of view. This article presents spectral equations for the estimation of horizontal strong ground motions caused by shallow crustal earthquakes with magnitude range of Mw 5.0 to 7.4 and distance to the surface projection of the fault less than 100 km for theoretical (simulated) records. The reason for development of ground motion in this region is that strong ground motion data are too sparse to allow ground motion relations to be derived directly from sufficient observed data. By considering the modeling parameters, we have used the stochastic finite fault modeling to generate a large suite of acceleration time histories for this region. The attenuation characteristics of horizontal spectral accelerations of strong motion in near-field are studied in this paper and the attenuation relations for horizontal acceleration response spectrum in the period range of 0.1–5 s for rock classification in the East-Central Iran are established. These equations were derived by two-stage regression analysis, on a set of 1200 theoretical strong-motion records generated in this area. The present results will be useful in estimating strong ground motion parameters and in the earthquake resistant design in the East-Central Iran region

The Stroke Neuro-Imaging Phenotype Repository: An open data science platform for stroke research

Stroke is one of the leading causes of death and disability worldwide. Reducing this disease burden through drug discovery and evaluation of stroke patient outcomes requires broader characterization of stroke pathophysiology, yet the underlying biologic and genetic factors contributing to outcomes are largely unknown. Remedying this critical knowledge gap requires deeper phenotyping, including large-scale integration of demographic, clinical, genomic, and imaging features. Such big data approaches will be facilitated by developing and running processing pipelines to extract stroke-related phenotypes at large scale. Millions of stroke patients undergo routine brain imaging each year, capturing a rich set of data on stroke-related injury and outcomes. The Stroke Neuroimaging Phenotype Repository (SNIPR) was developed as a multi-center centralized imaging repository of clinical computed tomography (CT) and magnetic resonance imaging (MRI) scans from stroke patients worldwide, based on the open source XNAT imaging informatics platform. The aims of this repository are to: (i) store, manage, process, and facilitate sharing of high-value stroke imaging data sets, (ii) implement containerized automated computational methods to extract image characteristics and disease-specific features from contributed images, (iii) facilitate integration of imaging, genomic, and clinical data to perform large-scale analysis of complications after stroke; and (iv) develop SNIPR as a collaborative platform aimed at both data scientists and clinical investigators. Currently, SNIPR hosts research projects encompassing ischemic and hemorrhagic stroke, with data from 2,246 subjects, and 6,149 imaging sessions from Washington University\u27s clinical image archive as well as contributions from collaborators in different countries, including Finland, Poland, and Spain. Moreover, we have extended the XNAT data model to include relevant clinical features, including subject demographics, stroke severity (NIH Stroke Scale), stroke subtype (using TOAST classification), and outcome [modified Rankin Scale (mRS)]. Image processing pipelines are deployed on SNIPR using containerized modules, which facilitate replicability at a large scale. The first such pipeline identifies axial brain CT scans from DICOM header data and image data using a meta deep learning scan classifier, registers serial scans to an atlas, segments tissue compartments, and calculates CSF volume. The resulting volume can be used to quantify the progression of cerebral edema after ischemic stroke. SNIPR thus enables the development and validation of pipelines to automatically extract imaging phenotypes and couple them with clinical data with the overarching aim of enabling a broad understanding of stroke progression and outcomes

Hybrid-Empirical Ground Motion Estimations for Georgia

Ground motion prediction equations are essential for several purposes ranging from seismic design and analysis to probabilistic seismic hazard assessment. In seismically active regions without sufficiently strong ground motion data to build empirical models, hybrid models become vital. Georgia does not have sufficiently strong ground motion data to build empirical models. In this study, we have applied the host-totarget method in two regions in Georgia with different source mechanisms. According to the tectonic regime of the target areas, two different regions are chosen as host regions. One of them is in Turkey with the dominant strike-slip source mechanism, while the other is in Iran with the prevalence of reverse-mechanism events. We performed stochastic finite-fault simulations in both host and target areas and employed the hybrid-empirical method as introduced in Campbell (2003). An initial set of hybrid empirical ground motion estimates is obtained for PGA and SA at selected periods for Georgia

A Ground-Motion Predictive Model for Iran and Turkey for Horizontal PGA, PGV, and 5% Damped Response Spectrum: Investigation of Possible Regional Effects

We present a ground-motion prediction equation (GMPE) for Turkey and Iran to investigate the possible regional effects on ground-motion amplitudes in shallow active crustal earthquakes. The proposed GMPE is developed from a subset of the recently compiled strong-motion database of the Earthquake Model of the Middle East Region project (see Data and Resources). A total of 670 Turkish and 528 Iranian accelerograms with depths down to 35 km are used to estimate peak ground acceleration, peak ground velocity, and 5% damped elastic pseudospectral acceleration ordinates of 0: 01 s <= T <= 4 s. The moment magnitude range of the model is 4 <= M-w <= 8, and the maximum Joyner-Boore distance is R-JB = 200 km. The functional form considers three major fault mechanisms (strike slip, normal, and reverse). The nonlinear soil behavior is a function of V-S30 (average shear-wave velocity in the upper 30 m of soil profile). Our observations from empirical and estimated ground-motion trends advocate regional differences in the territories covered by Iran and Turkey that originate from the differences in Q factors, kappa, and near-surface velocity profiles. These factors eventually affect the magnitude- and distance-dependent scaling of spectral amplitudes in Iran and Turkey. In essence, the ground-motion amplitudes of these two neighboring countries would draw patterns different than the ground-motion estimates of GMPEs developed from the strong-motion databases of shallow active crustal earthquakes from multiple countries

Quantitative Serial CT Imaging-Derived Features Improve Prediction of Malignant Cerebral Edema after Ischemic Stroke

INTRODUCTION: Malignant cerebral edema develops in a small subset of patients with hemispheric strokes, precipitating deterioration and death if decompressive hemicraniectomy (DHC) is not performed in a timely manner. Predicting which stroke patients will develop malignant edema is imprecise based on clinical data alone. Head computed tomography (CT) imaging is often performed at baseline and 24-hours. We determined the incremental value of incorporating imaging-derived features from serial CTs to enhance prediction of malignant edema. METHODS: We identified hemispheric stroke patients at three sites with NIHSS ≥ 7 who had baseline as well as 24-hour clinical and CT-imaging data. We extracted quantitative imaging features from baseline and follow-up CTs, including CSF volume, intracranial reserve (CSF/cranial volume), as well as midline shift (MLS) and infarct-related hypodensity volume. Potentially lethal malignant edema was defined as requiring DHC or dying with MLS over 5-mm. We built machine-learning models using logistic regression first with baseline data and then adding 24-hour data including reduction in CSF volume (ΔCSF). Model performance was evaluated with cross-validation using metrics of recall (sensitivity), precision (predictive value), as well as area under receiver-operating-characteristic and precision-recall curves (AUROC, AUPRC). RESULTS: Twenty of 361 patients (6%) died or underwent DHC. Baseline clinical variables alone had recall of 60% with low precision (7%), AUROC 0.59, AUPRC 0.15. Adding baseline intracranial reserve improved recall to 80% and AUROC to 0.82 but precision remained only 16% (AUPRC 0.28). Incorporating ΔCSF improved AUPRC to 0.53 (AUROC 0.91) while all imaging features further improved prediction (recall 90%, precision 38%, AUROC 0.96, AUPRC 0.66). CONCLUSION: Incorporating quantitative CT-based imaging features from baseline and 24-hour CT enhances identification of patients with malignant edema needing DHC. Further refinements and external validation of such imaging-based machine learning models are required

The 2014 seismic hazard model of the Middle East: overview and results

The Earthquake Model of Middle East (EMME) Project aimed to develop regional scale seismic hazard and risk models uniformly throughout a region extending from the Eastern Mediterranean in the west to the Himalayas in the east and from the Gulf of Oman in the south to the Greater Caucasus in the North; a region which has been continuously devastated by large earthquakes throughout the history. The 2014 Seismic Hazard Model of Middle East (EMME-SHM14) was developed with the contribution of several institutions from ten countries. The present paper summarizes the efforts towards building a homogeneous seismic hazard model of the region and highlights some of the main results of this model. An important aim of the project was to transparently communicate the data and methods used and to obtain reproducible results. By doing so, the use of the model and results will be accessible by a wide community, further support the mitigation of seismic risks in the region and facilitate future improvements to the seismic hazard model. To this end all data, results and methods used are made available through the web-portal of the European Facilities for Earthquake Hazard and Risk (www.efehr.org)