SAdaBoundNc: an adaptive subgradient online learning algorithm with logarithmic regret bounds

Neural Computing and Applications - Adaptive (sub)gradient methods have received wide applications such as the training of deep networks. The square-root regret bounds are achieved in convex...     

Macrophage Membrane-Coated Nanoparticles Sensitize Glioblastoma to Radiation by Suppressing Proneural–Mesenchymal Transformation in Glioma Stem Cells

Radiotherapy is identified as a crucial treatment for patients with glioblastoma, but recurrence is inevitable. The efficacy of radiotherapy is severely hampered partially due to the tumor evolution. Growing evidence suggests that proneural glioma stem cells can acquire mesenchymal features coupled with increased radioresistance. Thus, a better understanding of mechanisms underlying tumor subclonal evolution may develop new strategies. Herein, data highlighting a positive correlation between the accumulation of macrophage in the glioblastoma microenvironment after irradiation and mesenchymal transdifferentiation in glioblastoma are presented. Mechanistically, elevated production of inflammatory cytokines released by macrophages promotes mesenchymal transition in an NF-κB-dependent manner. Hence, rationally designed macrophage membrane-coated porous mesoporous silica nanoparticles (MMNs) in which therapeutic anti-NF-κB peptides are loaded for enhancing radiotherapy of glioblastoma are constructed. The combination of MMNs and fractionated irradiation results in the blockage of tumor evolution and therapy resistance in glioblastoma-bearing mice. Intriguingly, the macrophage invasion across the blood-brain barrier is inhibited competitively by MMNs, suggesting that these nanoparticles can fundamentally halt the evolution of radioresistant clones. Taken together, the biomimetic MMNs represent a promising strategy that prevents mesenchymal transition and improves therapeutic response to irradiation as well as overall survival in patients with glioblastoma.     

Proximity-Induced Fully Ferromagnetic Order with Eightfold Magnetic Anisotropy in Heavy Transition Metal Oxide CaRuO3

Ferromagnetic materials with a strong spin-orbit coupling (SOC) have attracted much attention in recent years because of their exotic properties and potential applications in energy-efficient spintronics. However, such materials are scarce in nature. Here, a proximity-induced paramagnetic to ferromagnetic transition for the heavy transition metal oxide CaRuO₃ in (001)-(LaMnO₃/CaRuO₃) superlattices is reported. Anomalous Hall effect is observed in the temperature range up to 180 K. Maximal anomalous Hall conductivity and anomalous Hall angle are as large as ∼15 Ω⁻¹ cm⁻¹ and ∼0.93%, respectively, by one to two orders of magnitude larger than those of the typical 3d ferromagnetic oxides such as La_0.67Sr_0.33MnO₃. Density functional theory calculations indicate the existence of avoid band crossings in the electronic band structure of the ferromagnetic CRO layer, which enhances Berry curvature thus strong anomalous Hall effects. Further evidences from polarized neutron reflectometry show that the CaRuO₃ layers are in a fully ferromagnetic state (∼0.8 μ_B/Ru), in sharp contrast to the proximity-induced canted antiferromagnetic state in 5d oxides SrIrO₃ and CaIrO₃ (∼0.1 μ_B/Ir). More than that, the magnetic anisotropy of the (001)-(LaMnO₃/CaRuO₃) superlattices is eightfold symmetric, showing potential applications in the technology of multistate data storage.     

Semi-supervised transformable architecture search for feature distillation

Pattern Analysis and Applications - The designed method aims to perform image classification tasks efficiently and accurately. Different from the traditional CNN-based image classification methods,...     

An optimized image watermarking algorithm based on SVD and IWT

The Journal of Supercomputing - With the wide spread of image information, it is an urgent problem to protect image property rights and crack down on piracy. Watermarking algorithm is an effective...     

Enhanced semantic representation learning for implicit discourse relation classification

Applied Intelligence - Implicit discourse relation classification is one of the most challenging tasks in discourse parsing. Without connectives as linguistic clues, classifying discourse relations...     

The impact of industrial agglomeration on industrial pollutant emission: evidence from China under New Normal

The purpose of this study is to analyze the environmental pollution effects elicited by industrial agglomeration and to devise necessary changes before and after China going into the New Normal, a contemporary phase of less rapid but more sustainable economic development. An empirical model is constructed based on the Copeland–Taylor model, and empirical research is conducted using statistical panel data derived from 285 Chinese cities between 2003 and 2014. To study the relationship between industrial agglomeration and industrial pollutant emission both before and after the ‘New Normal,’ the sample data are divided into two time periods: 2003–2008 and 2009–2014. Estimated results are as follows. First, industrial agglomeration exacerbates industrial pollution levels overall although the negative environmental effect of industrial agglomeration is weakened following China’s entry into the New Normal phase of economy. Second, both the interaction term of industrial agglomeration and foreign direct investment (FDI) and the interaction term of industrial agglomeration and environmental regulation are negatively related to industrial agglomeration. These findings indicate that FDI and environmental regulation can indirectly reduce industrial pollutant emissions by way of industrial agglomeration.     

Proline-derived in situ synthesis of nitrogen-doped porous carbon nanosheets with encaged Fe2O3@Fe3C nanoparticles for lithium-ion battery anodes

The homogeneous incorporation of heteroatoms into two-dimensional C nanostructures, which leads to an increased chemical reactivity and electrical conductivity as well as enhanced synergistic catalysis as a conductive matrix to disperse and encapsulate active nanocatalysts, is highly attractive and quite challenging. In this study, by using the natural and cheap hydrotropic amino acid proline—which has remarkably high solubility in water and a desirable N content of ~12.2 wt.%—as a C precursor pyrolyzed in the presence of a cubic KCl template, we developed a facile protocol for the large-scale production of N-doped C nanosheets with a hierarchically porous structure in a homogeneous dispersion. With concomitantly encapsulated and evenly spread Fe₂O₃ nanoparticles surrounded by two protective ultrathin layers of inner Fe₃C and outer onion-like C, the resulting N-doped graphitic C nanosheet hybrids (Fe₂O₃@Fe₃C-NGCNs) exhibited a very high Li-storage capacity and excellent rate capability with a reliable and prolonged cycle life. A reversible capacity as high as 857 mAh•g^–1 at a current density of 100 mA•g^–1 was observed even after 100 cycles. The capacity retention at a current density 10 times higher—1,000 mA•g^–1—reached 680 mAh•g^–1, which is 79% of that at 100 mA•g^–1, indicating that the hybrids are promising as anodes for advanced Li-ion batteries. The results highlight the importance of the heteroatomic dopant modification of the NGCNs host with tailored electronic and crystalline structures for competitive Li-storage features.