Pre-Earthquake Ionospheric Perturbation Identification Using CSES Data \u3cem\u3evia\u3c/em\u3e Transfer Learning

During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky radius and an input sequence length of 20 consecutive observations during night time. We further explore a transferring learning approach, which initially trains the model with the larger Electro-Magnetic Emissions Transmitted from the Earthquake Regions (DEMETER) dataset, and then tunes the model with the CSES dataset. The transfer-learning performance is substantially higher than that of direct learning, yielding a 12% improvement in the F1 score and a 29% improvement in the MCC value. Moreover, we compare the proposed model SeqNetQuake with other five benchmarking classifiers on an independent test set, which shows that SeqNetQuake demonstrates a 64.2% improvement in MCC and approximately a 24.5% improvement in the F1 score over the second-best convolutional neural network model. SeqNetSquake achieves significant improvement in identifying pre-earthquake ionospheric perturbation and improves the performance of earthquake prediction using the CSES data

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. Among 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 1371 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

hSef potentiates EGF-mediated MAPK signaling through affecting EGFR trafficking and degradation

Sef (similar expression to fgf genes) was identified as an effective antagonist of fibroblast growth factor (FGF) in vertebrates. Previous reports have demonstrated that Sef interacts with FGF receptors (FGFRs) and inhibits FGF signaling, however, its role in regulating epidermal growth factor receptor (EGFR) signaling remains unclear. In this report, we found that hSef localizes to the plasma membrane (PM) and is subjected to rapid internalization and well localizes in early/recycling endosomes while poorly in late endosomes/lysosomes. We observed that hSef interacts and functionally colocalizes with EGFR in early endosomes in response to EGF stimulation. Importantly, we demonstrated that overexpression of hSef attenuates EGFR degradation and potentiates EGF-mediated mitogen-activated protein kinase (MAPK) signaling by interfering EGFR trafficking. Finally, our data showed that, with overexpression of hSef, elevated levels of Erk phosphorylation and differentiation of rat pheochromocytoma (PC12) cells occur in response to EGF stimulation. Taken together, these data suggest that hSef plays a positive role in the EGFR-mediated MAPK signaling pathway. This report, for the first time, reveals opposite roles for Sef in EGF and FGF signalings

Realization of Qi-Wu-Zhang model in spin-orbit-coupled ultracold fermions

Based on the optical Raman lattice technique, we experimentally realize the Qi-Wu-Zhang model for quantum anomalous Hall phase in ultracold fermions with two-dimensional (2D) spin-orbit (SO) coupling. We develop a novel protocol of pump-probe quench measurement to probe, with minimal heating, the resonant spin flipping on particular quasi-momentum subspace called band-inversion surfaces. With this protocol we demonstrate the first Dirac-type 2D SO coupling in a fermionic system, and detect non-trivial band topology by observing the change of band-inversion surfaces as the two-photon detuning varies. The non-trivial band topology is also observed by slowly loading the atoms into optical Raman lattices and measuring the spin textures. Our results show solid evidence for the realization of the minimal SO-coupled quantum anomalous Hall model, which can provide a feasible platform to investigate novel topological physics including the correlation effects with SO-coupled ultracold fermions.Comment: 7 pages, 4 figures in the main text, and 6 pages, 3 figures in the supplemental materia

Coseeded Schwann cells myelinate neurites from differentiated neural stem cells in neurotrophin-3-loaded PLGA carriers

Biomaterials and neurotrophic factors represent promising guidance for neural repair. In this study, we combined poly-(lactic acid-co-glycolic acid) (PLGA) conduits and neurotrophin-3 (NT-3) to generate NT-3-loaded PLGA carriers in vitro. Bioactive NT-3 was released stably and constantly from PLGA conduits for up to 4 weeks. Neural stem cells (NSCs) and Schwann cells (SCs) were coseeded into an NT-releasing scaffold system and cultured for 14 days. Immunoreactivity against Map2 showed that most of the grafted cells (>80%) were differentiated toward neurons. Double-immunostaining for synaptogenesis and myelination revealed the formation of synaptic structures and myelin sheaths in the coculture, which was also observed under electron microscope. Furthermore, under depolarizing conditions, these synapses were excitable and capable of releasing synaptic vesicles labeled with FM1-43 or FM4-64. Taken together, coseeding NSCs and SCs into NT-3-loaded PLGA carriers increased the differentiation of NSCs into neurons, developed synaptic connections, exhibited synaptic activities, and myelination of neurites by the accompanying SCs. These results provide an experimental basis that supports transplantation of functional neural construction in spinal cord injury

Mechanisms of oral bacterial virulence factors in pancreatic cancer

Pancreatic cancer is a highly lethal disease, and most patients remain asymptomatic until the disease enters advanced stages. There is lack of knowledge in the pathogenesis, effective prevention and early diagnosis of pancreatic cancer. Recently, bacteria were found in pancreatic tissue that has been considered sterile before. The distribution of flora in pancreatic cancer tissue was reported to be different from normal pancreatic tissue. These abnormally distributed bacteria may be the risk factors for inducing pancreatic cancer. Therefore, studies on combined effect of multi-bacterial and multi-virulence factors may add to the knowledge of pancreatic cancer pathogenesis and aid in designing new preventive and therapeutic strategies. In this review, we outlined three oral bacteria associated with pancreatic cancer and their virulence factors linked with cancer