GNSS TEC-based earthquake ionospheric perturbation detection using a novel deep learning framework

In this article, a new method for seismic ionospheric Global Navigation Satellite System (GNSS) total electron content (TEC) based anomaly detection using a deep learning framework is proposed. The performance of the proposed encoder–decoder long short-term memory extended model is compared with those of five other benchmarking predictors. The proposed model achieves the best performance (R2 = 0.9105 and root-mean-square error (RMSE) = 2.6759) in predicting TEC time series data, demonstrating a 20% improvement in R2 and 39.1% improvement in the RMSE over the autoregressive integrated moving average model. To detect the pre-earthquake ionospheric disturbances more accurately, a reasonable strategy for determining anomaly limits is also proposed. Finally, the method is applied to analyze the pre-earthquake ionospheric TEC disturbance of the 2016 Xinjiang Ms 6.2 earthquake. By excluding the effects of solar activity and geomagnetic activity, obvious ionospheric anomalies could be observed, occurring during 4–8 days prior to, and on 1 day before, the earthquake. Negative anomalies tended to occur in the earlier period, whereas positive anomalies occurred closer to the earthquake time, with increasing anomaly intensity with temporal proximity. Furthermore, confusion analysis is used in this article to verify the reliability of the proposed model. The proposed model achieves significant improvements in predicting GNSS TEC time series and is shown to advance the performance of earthquake anomaly detection technology.</p

Long Short-Term Memory Neural Network for Ionospheric Total Electron Content 1 Forecasting over China

An increasing number of terrestrial- and space-based radio-communication systems are influenced by the ionospheric space weather, making the ionospheric state increasingly important to forecast. In this study, a novel extended encoder-decoder long short-term memory extended (ED-LSTME) neural network, which can predict ionospheric total electron content (TEC) is proposed. Useful inherent features were automatically extracted from the historical TEC by LSTM layers, and the performance of the proposed model was enhanced by considering solar flux and geomagnetic activity data. The proposed ED-LSTME model was validated using 15-min TEC values from GPS measurements over one solar cycle (from January 2006 to July 2018) collected at 15 GPS stations in China. Different assessment experiments were conducted in different geographical locations and seasons as well as under varying geomagnetic activities, to comprehensively evaluate the model's performance. These comparative experiments were conducted using an ED-LSTM, a traditional LSTM, a deep neural network, autoregressive integrated moving average, and the 2016 International Reference Ionosphere models. The results indicated that the ED-LSTME model is superior to the other statistical models, with R2 and root mean square error values of 0.89 and 12.09 TECU, respectively. In addition, TEC was adequately predicted under different ionospheric conditions, and satisfactory results were obtained even under geomagnetically disturbed conditions. These results suggest that the prediction performance could be significantly improved by utilizing auxiliary data. These observations confirm that the proposed model outperforms several state-of-the-art models in making predictions at different times and under diverse conditions

Identification of electromagnetic pre-earthquake perturbations from the DEMETER data by machine learning

The low-altitude satellite DEMETER recorded many cases of ionospheric perturbations observed on occasion of large seismic events. In this paper, we explore 16 spot-checking classification algorithms, among which, the top classifier with low-frequency power spectra of electric and magnetic fields was used for ionospheric perturbation analysis. This study included the analysis of satellite data spanning over six years, during which about 8760 earthquakes with magnitude greater than or equal to 5.0 occurred in the world. We discover that among these methods, a gradient boosting-based method called LightGBM outperforms others and achieves superior performance in a five-fold cross-validation test on the benchmarking datasets, which shows a strong capability in discriminating electromagnetic pre-earthquake perturbations. The results show that the electromagnetic pre-earthquake data within a circular region with its center at the epicenter and its radius given by the Dobrovolsky’s formula and the time window of about a few hours before shocks are much better at discriminating electromagnetic pre-earthquake perturbations. Moreover, by investigating different earthquake databases, we confirm that some low-frequency electric and magnetic fields’ frequency bands are the dominant features for electromagnetic pre-earthquake perturbations identification. We have also found that the choice of the geographical region used to simulate the training set of non-seismic data influences, to a certain extent, the performance of the LightGBM model, by reducing its capability in discriminating electromagnetic pre-earthquake perturbations

Manipulation of the internal structure of high amylose maize starch by high pressure treatment and its diverse influence on digestion

© 2017 Elsevier Ltd. In this study, high amylose maize starch was mixed with different moisture contents, followed by high hydrostatic pressure (HHP) at 200, 400, 600, 800 and 1000 MP, respectively. Changes in starch physicochemical and digestion properties associated with HHP treatment were analyzed in terms of starch granular morphology, lamellar structures and crystalline characteristics. Results showed that, under the same pressure treatments, the starches with different moisture contents exhibited a similar pattern of the changes in the properties. The erosion of digestive enzymes on starch granules was enhanced with increased HPP pressures. Treatment with 200 and 400 MP led to a reduction of digestibility compared to its native one. However, digestion was gradually promoted when the treatment pressure reached up to 600 MP. Structural data acquired from SAXS and WAX indicated the treatment of HHP up to 600 MP partly destroyed the starch granules internally, resulting in a decreased degree of organized structure. These results may reveal the importance of starch lamellar structure and crystalline order as being the key structural parameters for influencing starch digestion properties. Changes in the electron density following the digestion indicated that digestion characteristics of the starch are highly related to the changes in its corresponding internal structure of amylopectin amorphous layer, amylose amorphous and amylopectin crystal layer caused by HPP. Further analysis of the changes in the relative crystallinity of the starch may suggest that starch digestion characteristics are highly related to lamellar structure but not relative crystallinity

ChainCluster: Engineering a cooperative content distribution framework for highway vehicular communications

The recent advances in wireless communication techniques have made it possible for fast-moving vehicles to download data from the roadside communications infrastructure [e.g., IEEE 802.11b Access Point (AP)], namely, Drive-thru Internet. However, due to the high mobility, harsh, and intermittent wireless channels, the data download volume of individual vehicle per drive-thru is quite limited, as observed in real-world tests. This would severely restrict the service quality of upper layer applications, such as file download and video streaming. On addressing this issue, in this paper, we propose ChainCluster, a cooperative Drive-thru Internet scheme. ChainCluster selects appropriate vehicles to form a linear cluster on the highway. The cluster members then cooperatively download the same content file, with each member retrieving one portion of the file, from the roadside infrastructure. With cluster members consecutively driving through the roadside infrastructure, the download of a single vehicle is virtually extended to that of a tandem of vehicles, which accordingly enhances the probability of successful file download significantly. With a delicate linear cluster formation scheme proposed and applied, in this paper, we first develop an analytical framework to evaluate the data volume that can be downloaded using cooperative drive-thru. Using simulations, we then verify the performance of ChainCluster and show that our analysis can match the simulations well. Finally, we show that ChainCluster can outperform the typical studied clustering schemes and provide general guidance for cooperative content distribution in highway vehicular communications

Towards advancing the earthquake forecasting by machine learning of satellite data

Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Amongst the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1,371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases

Endovascular thrombectomy versus standard bridging thrombolytic with endovascular thrombectomy within 4·5 h of stroke onset: an open-label, blinded-endpoint, randomised non-inferiority trial

Background: The benefit of combined treatment with intravenous thrombolysis before endovascular thrombectomy in patients with acute ischaemic stroke caused by large vessel occlusion remains unclear. We hypothesised that the clinical outcomes of patients with stroke with large vessel occlusion treated with direct endovascular thrombectomy within 4·5 h would be non-inferior compared with the outcomes of those treated with standard bridging therapy (intravenous thrombolysis before endovascular thrombectomy). Methods: DIRECT-SAFE was an international, multicentre, prospective, randomised, open-label, blinded-endpoint trial. Adult patients with stroke and large vessel occlusion in the intracranial internal carotid artery, middle cerebral artery (M1 or M2), or basilar artery, confirmed by non-contrast CT and vascular imaging, and who presented within 4·5 h of stroke onset were recruited from 25 acute-care hospitals in Australia, New Zealand, China, and Vietnam. Eligible patients were randomly assigned (1:1) via a web-based, computer-generated randomisation procedure stratified by site of baseline arterial occlusion and by geographic region to direct endovascular thrombectomy or bridging therapy. Patients assigned to bridging therapy received intravenous thrombolytic (alteplase or tenecteplase) as per standard care at each site; endovascular thrombectomy was also per standard of care, using the Trevo device (Stryker Neurovascular, Fremont, CA, USA) as first-line intervention. Personnel assessing outcomes were masked to group allocation; patients and treating physicians were not. The primary efficacy endpoint was functional independence defined as modified Rankin Scale score 0–2 or return to baseline at 90 days, with a non-inferiority margin of –0·1, analysed by intention to treat (including all randomly assigned and consenting patients) and per protocol. The intention-to-treat population was included in the safety analyses. The trial is registered with ClinicalTrials.gov, NCT03494920, and is closed to new participants. Findings: Between June 2, 2018, and July 8, 2021, 295 patients were randomly assigned to direct endovascular thrombectomy (n=148) or bridging therapy (n=147). Functional independence occurred in 80 (55%) of 146 patients in the direct thrombectomy group and 89 (61%) of 147 patients in the bridging therapy group (intention-to-treat risk difference –0·051, two-sided 95% CI –0·160 to 0·059; per-protocol risk difference –0·062, two-sided 95% CI –0·173 to 0·049). Safety outcomes were similar between groups, with symptomatic intracerebral haemorrhage occurring in two (1%) of 146 patients in the direct group and one (1%) of 147 patients in the bridging group (adjusted odds ratio 1·70, 95% CI 0·22–13·04) and death in 22 (15%) of 146 patients in the direct group and 24 (16%) of 147 patients in the bridging group (adjusted odds ratio 0·92, 95% CI 0·46–1·84). Interpretation: We did not show non-inferiority of direct endovascular thrombectomy compared with bridging therapy. The additional information from our study should inform guidelines to recommend bridging therapy as standard treatment. Funding: Australian National Health and Medical Research Council and Stryker USA

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease