Statistical descriptions of earthquakes offer important probabilistic information, and newly emerging technologies of high-precision observations and machine learning collectively advance our ...knowledge regarding complex earthquake behaviors. Still, there remains a formidable knowledge gap for predicting individual large earthquakes' locations and magnitudes. Here, this study shows that the individual large earthquakes may have unique signatures that can be represented by new high-dimensional features-Gauss curvature-based coordinates. Particularly, the observed earthquake catalog data are transformed into a number of pseudo physics quantities (i.e., energy, power, vorticity, and Laplacian) which turn into smooth surface-like information via spatio-temporal convolution, giving rise to the new high-dimensional coordinates. Validations with 40-year earthquakes in the West U.S. region show that the new coordinates appear to hold uniqueness for individual large earthquakes (Formula: see text), and the pseudo physics quantities help identify a customized data-driven prediction model. A Bayesian evolutionary algorithm in conjunction with flexible bases can identify a data-driven model, demonstrating its promising reproduction of individual large earthquake's location and magnitude. Results imply that an individual large earthquake can be distinguished and remembered while its best-so-far model can be customized by machine learning. This study paves a new way to data-driven automated evolution of individual earthquake prediction.
Abstract
Predicting individual large earthquakes (EQs)’ locations, magnitudes, and timing remains unreachable. The author’s prior study shows that individual large EQs have unique signatures obtained ...from multi-layered data transformations. Via spatio-temporal convolutions, decades-long EQ catalog data are transformed into pseudo-physics quantities (e.g., energy, power, vorticity, and Laplacian), which turn into surface-like information via Gauss curvatures. Using these new features, a rule-learning machine learning approach unravels promising prediction rules. This paper suggests further data transformation via Fourier transformation (FT). Results show that FT-based new feature can help sharpen the prediction rules. Feasibility tests of large EQs (
$$M\ge$$
M
≥
6.5) over the past 40 years in the western U.S. show promise, shedding light on data-driven prediction of individual large EQs. The handshake among ML methods, Fourier, and Gauss may help answer the long-standing enigma of seismogenesis.
▶ Treadmill exercise ameliorates cognitive deficits in Tg mice. ▶ Treadmill exercise reduces Aβ-42 and tau deposition in Tg mice. ▶ Treadmill exercise reduces the number of TUNEL-positive cells in Tg ...mice. ▶ Treadmill exercise reduces TC, insulin, glucose, and corticosterone levels in Tg mice. ▶ Treadmill exercise may be beneficial in prevention or treatment in AD.
The present study was undertaken to further investigate the protective effect of treadmill exercise on the hippocampal proteins associated with neuronal cell death in an aged transgenic (Tg) mice with Alzheimer's disease (AD). To address this, Tg mouse model of AD, Tg-NSE/PS2m, which expresses human mutant PS2 in the brain, was chosen. Animals were subjected to treadmill exercise for 12 weeks from 24 months of age. The exercised mice were treadmill run at speed of 12
m/min, 60
min/day, 5 days/week on a 0% gradient for 3 months. Treadmill exercised mice improved cognitive function in water maze test. Treadmill exercised mice significantly reduced the expression of Aβ-42, Cox-2, and caspase-3 in the hippocampus. In parallel, treadmill exercised Tg mice decreased the phosphorylation levels of JNK, p38MAPK and tau (Ser404, Ser202, Thr231), and increased the phosphorylation levels of ERK, PI3K, Akt and GSK-3α/β. In addition, treadmill exercised Tg mice up-regulated the expressions of NGF, BDNF and phospho-CREB, and the expressions of SOD-1, SOD-2 and HSP-70. Treadmill exercised Tg mice up-regulated the expression of Bcl-2, and down-regulated the expressions of cytochrome c and Bax in the hippocampus. The number of TUNEL-positive cells in the hippocampus in mice was significantly decreased after treadmill exercise. Finally, serum TC, insulin, glucose, and corticosterone levels were significantly decreased in the Tg mice after treadmill exercise. As a consequence of such change, Aβ-dependent neuronal cell death in the hippocampus of Tg mice was markedly suppressed following treadmill exercise. These results strongly suggest that treadmill exercise provides a therapeutic potential to inhibit both Aβ-42 and neuronal death pathways. Therefore, treadmill exercise may be beneficial in prevention or treatment of AD.
Atherosclerosis is an inflammatory process, and inflammatory biomarkers have been identified as useful predictors of clinical outcomes. The prognostic value of leukocyte count in patients with ...ST-segment elevation myocardial infarctions who undergo primary percutaneous coronary intervention is not clearly defined. In 325 patients with STEMIs treated with primary percutaneous coronary intervention, total and differential leukocyte counts, once at admission and 24 hours thereafter, were measured. The neutrophil/lymphocyte ratio (NLR) was calculated as the ratio of neutrophil count to lymphocyte count. The primary end point was all-cause death. Twenty-five patients (7.7%) died during follow-up (median 1,092 days, interquartile range 632 to 1,464). The total leukocyte count decreased (from 11,853 ± 3,946/μl to 11,245 ± 3,979/μl, p = 0.004) from baseline to 24 hours after admission. Patients who died had higher neutrophil counts (9,887 ± 5,417/μl vs 8,399 ± 3,639/μl, p = 0.061), lower lymphocyte counts (1,566 ± 786/μl vs 1,899 ± 770/μl, p = 0.039), and higher NLRs (8.58 ± 7.41 vs 5.51 ± 4.20, p = 0.001) at 24 hours after admission. Baseline leukocyte profile was not associated with outcomes. The best cut-off value of 24-hour NLR to predict mortality was 5.44 (area under the curve 0.72, 95% confidence interval CI 0.52 to 0.82). In multivariate analysis, a 24-hour NLR ≥5.44 was an independent predictor of mortality (hazard ratio 3.12, 95% CI 1.14 to 8.55), along with chronic kidney disease (hazard ratio 4.23, 95% CI 1.62 to 11.1) and the left ventricular ejection fraction (hazard ratio 0.94 for a 3% increase, 95% CI 0.76 to 0.93). In conclusion, NLR at 24 hours after admission can be used for risk stratification in patients with STEMIs who undergo primary PCI. Patients with STEMIs with 24-hour NLRs ≥5.44 are at increased risk for mortality and should receive more intensive treatment.
Summary
There exists a deep chasm between machine learning (ML) and high‐fidelity computational material models in science and engineering. Due to the complex interaction of internal physics, ML ...methods hardly conquer or innovate them. To fill the chasm, this paper finds an answer from the central notions of deep learning (DL) and proposes information index and link functions, which are essential to infuse principles of physics into ML. Like the convolution process of DL, the proposed information index integrates adjacent information and quantifies the physical similarity between laboratory and reality, enabling ML to see through a complex target system with the perspective of scientists. Like the hidden layers' weights of DL, the proposed link functions unravel the hidden relations between information index and physics rules. Like the error backpropagation of DL, the proposed framework adopts fitness‐based spawning scheme of evolutionary algorithm. The proposed framework demonstrates that a fusion of information index, link functions, evolutionary algorithm, and Bayesian update scheme can engender self‐evolving computational material models and that the fusion will help rename ML as a partner of researchers in the broad science and engineering.
Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed ...discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-based prediction algorithms. This study included 222,998 Korean adults aged 40-79 years, naïve to lipid-lowering therapy, had no history of cardiovascular disease. Pre-existing models showed moderate to good discrimination in predicting future cardiovascular events (C-statistics 0.70-0.80). Pooled cohort equation (PCE) specifically showed C-statistics of 0.738. Among other machine learning models such as logistic regression, treebag, random forest, and adaboost, the neural network model showed the greatest C-statistic (0.751), which was significantly higher than that for PCE. It also showed improved agreement between the predicted risk and observed outcomes (Hosmer-Lemeshow χ
= 86.1, P < 0.001) than PCE for whites did (Hosmer-Lemeshow χ
= 171.1, P < 0.001). Similar improvements were observed for Framingham risk score, systematic coronary risk evaluation, and QRISK3. This study demonstrated that machine learning-based algorithms could improve performance in cardiovascular risk prediction over contemporary cardiovascular risk models in statin-naïve healthy Korean adults without cardiovascular disease. The model can be easily adopted for risk assessment and clinical decision making.
The growing demand for human-independent comfortable lifestyle has emboldened the development of smart home. A typical keenly intellective home includes many Internet of things contrivances that ...engender processes and immensely colossal data to efficiently handle its users’ demands. This incrementing demand raises a plethora of concern cognate to a smart home system in terms of scalability, efficiency, and security. All these issues are tedious to manage, and the existing studies lack the granularity for surmounting them. Considering such a requisite of security and efficiency as a quandary at hand, this article presents a secure and efficient smart home architecture, which incorporates the blockchain and the cloud computing technologies for a cumulated solution. Because of the decentralized nature of blockchain technology, it can serve the processing services and make the transaction copy of the collected sensible user data from smart home. To ensure the security of smart home network, our proposed model utilizes the multivariate correlation analysis technique to analyze the network traffic and identify the correlation between traffic features. We have evaluated the performance of our proposed architecture using different parameters like throughput and discovered that blockchain is an efficient security solution for the future Internet of things network.
The 2017 American College of Cardiology/American Heart Association (ACC/AHA) hypertension guideline lowered the threshold defining hypertension and treatment target from 140/90 mmHg to 130/80 mmHg. ...We compared the 2017 ACC/AHA guideline and the Eighth Joint National Committee (JNC8) report with regard to the current status of hypertension using the Korean National Health and Nutrition Examination Survey. The association between blood pressure (BP) control and long-term major cardiovascular outcomes (MACEs) was analyzed using the Korea National Health Insurance Service cohort. In the cross-sectional study with 15,784 adults, the prevalence of hypertension was expected to be 49.2 ± 0.6% based on the definition suggested by the 2017 ACC/AHA guideline versus 30.4 ± 0.6% based on the JNC8 report. In a longitudinal analysis with 373,800 hypertensive adults for the median follow-up periods of 11.0 years, the adults meeting the target goal BP goal of 2017 ACC/AHA guideline were associated with 21% reduced risk of MACEs compared with adults, not meeting 2017 ACC/AHA BP goal but meeting JNC8 target goal. In conclusion, substantial increase of prevalence of hypertension is expected by the 2017 ACC/AHA guideline. This study also suggests endorsing the aggressive approach would lead to an improvement in cardiovascular care.
This study aimed to investigate sex, age, and species differences of perfluorooctanoic acid (PFOA) using physiologically-based pharmacokinetic (PBPK) models in rats and humans. PBPK models were ...generally developed as either flow- or permeability-limited models. The flow-limited model is cost-effective and allows for human PK prediction through simple allometric scaling, while the permeability-limited model can incorporate detailed information on the disposition process through in vitro-in vivo extrapolation (IVIVE). PFOA was administered via oral or intravenous administration with 5 mg/kg in male and female rats of different ages and the data was used to develop the PBPK models. Our results showed that both models successfully captured sex differences in rats, while only the flow-limited model with male rats and the permeability-limited model with both male and female rats provided comparable predictions in the human clinical study. More than the flow-limited model, the permeability-limited model effectively explained sex differences in rats and species differences through IVIVE. Additionally, the ontogeny-based mechanistic description of PFOA disposition enabled the interpretation of age- and sex-dependent pharmacokinetics. Although the flow-limited PBPK model lacked mechanistic interpretability compared to the permeability-limited model, it demonstrated reliable human prediction through simple allometric scaling. In conclusion, the permeability PBPK model could interpret age, sex, and species differences and it could improve the accuracy of human prediction.
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This paper presents a half-bridge LLC resonant converter having a boost pulse width modulation (PWM) converter characteristic for hold-up state operation. The proposed converter is based on a ...half-bridge LLC resonant converter structure and a single auxiliary switch is added at the primary side. The converter has two different operational characteristics. It shows the same operational characteristic with the conventional LLC resonant converters during nominal state, which is frequency modulation (FM) method. However, when ac line lost and the converter enters into the hold-up time state, which requires wide voltage gain changes, the control method of the proposed converter is changed to the PWM method using the auxiliary switch. Since the proposed converter compensates wide voltage gain variation with PWM method of the auxiliary switch rather than adopting the FM method of main switches, the frequency variation range for the LLC resonant converter is highly reduced in the proposed converter. Therefore, the transformer in the proposed converter can be designed at the optimal operating point and it results in decreased conduction loss of the magnetizing inductor current. Furthermore, the maximum voltage gain of the proposed converter is easily increased by extending the duty ratio of the auxiliary switch. It helps to decrease the link capacitance. To verify the effectiveness of the proposed circuit, operational principle will be explained and experimental results will be presented with following specification. 100 kHz of switching frequency, 250-400 V of input voltage range, 250 V of output voltage, and 75 W output power.
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