Elaboration and characterization of nanoplate structured alpha-Fe2O3 films by Ag3PO4

A new strategy for surface treatment of hematite nanoplates for efficient photoelectrochemical (PEC) performances is proposed. Silver orthophosphate (Ag₃PO₄) has been adopted to mediate the formation of α-Fe₂O₃ films. Phosphate ions in Ag₃PO₄ is found to cause a significant morphology change during annealing process, from β-FeOOH nanorod arrays to hematite nanoplates. Meanwhile, Ag ions is doped into α-Fe₂O₃ film. The obtained nanoplate structured Fe₂O₃ –Ag–P films demonstrate much higher photoelectrochemical performance as photoanodes than the bare Fe₂O₃ nanorod thin films. The effects of phosphate and silver ions on the morphology, surface characteristics and the PEC properties of the photoanodes are investigated

A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations

We propose a model-data asymptotic-preserving neural network(MD-APNN) method to solve the nonlinear gray radiative transfer equations(GRTEs). The system is challenging to be simulated with both the traditional numerical schemes and the vanilla physics-informed neural networks(PINNs) due to the multiscale characteristics. Under the framework of PINNs, we employ a micro-macro decomposition technique to construct a new asymptotic-preserving(AP) loss function, which includes the residual of the governing equations in the micro-macro coupled form, the initial and boundary conditions with additional diffusion limit information, the conservation laws, and a few labeled data. A convergence analysis is performed for the proposed method, and a number of numerical examples are presented to illustrate the efficiency of MD-APNNs, and particularly, the importance of the AP property in the neural networks for the diffusion dominating problems. The numerical results indicate that MD-APNNs lead to a better performance than APNNs or pure data-driven networks in the simulation of the nonlinear non-stationary GRTEs

Curriculum Design Helps Spiking Neural Networks to Classify Time Series

Spiking Neural Networks (SNNs) have a greater potential for modeling time series data than Artificial Neural Networks (ANNs), due to their inherent neuron dynamics and low energy consumption. However, it is difficult to demonstrate their superiority in classification accuracy, because current efforts mainly focus on designing better network structures. In this work, enlighten by brain-inspired science, we find that, not only the structure but also the learning process should be human-like. To achieve this, we investigate the power of Curriculum Learning (CL) on SNNs by designing a novel method named CSNN with two theoretically guaranteed mechanisms: The active-to-dormant training order makes the curriculum similar to that of human learning and suitable for spiking neurons; The value-based regional encoding makes the neuron activity to mimic the brain memory when learning sequential data. Experiments on multiple time series sources including simulated, sensor, motion, and healthcare demonstrate that CL has a more positive effect on SNNs than ANNs with about twice the accuracy change, and CSNN can increase about 3% SNNs' accuracy by improving network sparsity, neuron firing status, anti-noise ability, and convergence speed.Comment: 11 pages, 3 figure

The Synthetic Compound Norcantharidin Induced Apoptosis in Mantle Cell Lymphoma In Vivo and In Vitro through the PI3K-Akt-NF- κ

This study aimed to elucidate the antitumor activity of norcantharidin (NCTD) against human mantle cell lymphoma (MCL). Cell proliferation and apoptosis were examined by MTS and flow cytometry. Caspase-3, -8, and -9 activities were detected with a colorimetric caspase protease assay. Apoptotic proteins—including PARP, cyclin D1, Bcl-2 family proteins, XIAP, and cIAP I—were studied by western blot. The phosphoinositide 3 kinase (PI3K) inhibitor LY294002 was used to investigate the involvement of the PI3K/Akt signaling pathway. In vivo studies were performed using Z138 cell xenografts in nude mice. NCTD inhibited proliferation and induced apoptosis of Z138 and Mino cells, both in vitro and in vivo. PI3Kp110α and p-Akt expressions were downregulated by NCTD treatment. NCTD downregulated NF-κB activity by preventing NF-κB phosphorylation and nuclear translocation. This effect was correlated with the suppression of NF-κB-regulated gene products, such as cyclin D1, BAX, survivin, Bcl-2, XIAP, and cIAP. This phenomenon was blocked by the PI3K inhibitor LY294002. Our results demonstrated that NCTD can induce growth arrest and apoptosis in MCL cells and that the mechanism may involve the PI3K/Akt/NF-κB signaling pathway. NCTD may have therapeutic and/or adjuvant therapeutic applications in the treatment of MCL

Tranquilizing and Allaying Excitement Needling Method Affects BDNF and SYP Expression in Hippocampus

Sleep disorder is a state of sleep loss caused by various reasons, which leads to a series of changes, such as emotion, learning and memory, and immune function. “Tranquilizing and allaying excitement” was widely used in clinical treatment of insomnia; however, the mechanism was still not very clear. We randomly divided rats into three groups: control group, sleep deprivation group, and acupuncture treatment group. We observed BDNF and SYP expression in hippocampus in these three groups. Both protein contents and mRNA contents of BDNF and SYP were measured by western blot, immunohistochemistry, and RT-PCR analysis. The sleep deprivation model was established using modified multiple platform sleep deprivation method (MMPM). Our study explored the BDNF and SYP abnormality in hippocampus caused by sleep deprivation and “tranquilizing and allaying excitement” intervention regulated the abnormal expression of BDNF and SYP caused by sleep deprivation on the short run and the long run. Our study provided a molecular evidence that “tranquilizing and allaying excitement” treatment in rats with sleep disorder affects learning and memory ability

Pyrene emission from monolayers 'clicked' onto quartz

A series of quartz surfaces were modifed with a series of crosslinkers and functional groups in order to obtain an azide-terminated monolayer, which was then used to immobilize pyrene onto the surface via alkyne-azide \click" chemistry. During the course of the immobilization, different ratios of tert-butyl diphenyl chlorosilane were used to control the distribution and hence the photophysical properties of the pyrene on the surface. The preparative surface reactions and photophysical properties were investigated with contact angle, X-ray photoelectron spectroscopy, UV-visible absorption and emission spectroscopy. High surface coverage was achieved of just under 1molecule per nm2. At this coverage all emission from the pyrene was in the form of excimer emission. Excimer emission dominated at all surface coverages greater than 0.45 molecules per nm2. Below this coverage the monomer emission could also be observed. The conclusions that can be drawn are important for understanding the interactions of neighboring molecules in molecular monolayers. Our results suggest that at high surface coverage a substantial number of the pyrene molecules are already close enough to their neighbors that pairs of them can be directly excited to form excimer with no requirement for diffusion. This can be stated because the long wavelength end of the pyrene absorption and excitation spectra show a broad tail that is assigned to a charge transfer band resulting from an electron being directly transferred from a ground state pyrene to a neighboring pyrene molecule. Furthermore, absorption spectra shifts also indicate that the pyrene molecules undergo some interactions on the surface when they are closely packed

Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image

Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Two novel modules named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth Reconstruction (SDDR) module are introduced to learn high fidelity object-level depth and delicate shape representations. At last, the 6D object pose is solved by aligning the predicted canonical representation with the back-projected object-level depth. Extensive experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model, though simple, achieves state-of-the-art performance.Comment: 19 pages, 7 figures, 4 table

In situ growth of ultrathin Co-MOF nanosheets on Α-Fe2O3 hematite nanorods for efficient photoelectrochemical water oxidation

Efficient charge transport is an important factor in photoelectrochemical (PEC) water splitting. The charge transfer at the semiconductor/electrolyte interface is of great importance, especially for the complex water oxidation reaction. In this study, we explored the feasibility of improving charge transfer efficiency at the interface of semiconductor/electrolyte by in situ growth of Co based Metal-Organic Frame work (Co-MOF) through a facile ion-exchanging method. Under optimized conditions, the Co-MOF nanosheet-modified hematite gave a photocurrent density of 2.0 mA cm−2 (200% improvement) at 1.23 VRHE with a cathodic shift of 180 mV in the photocurrent onset potential, in comparison to bare α-Fe2O3 (0.71 mA cm−[email protected] VRHE). To elucidate the role of Co-MOF, X-ray photoelectron spectroscopy, electrochemical impedance spectroscopy and Mott-Schottky measurements were carried out. It was found that the atomically distributed Co2+ in Co-MOF possessed excellent hole storage capability and charge transfer efficiency, as evidenced by the high surface capacitance and extremely low surface charge transfer resistance