MicroRNA-124 regulates apoptosis in sevoflurane anesthesia-induced neuroblastoma cells by targeting enhancer of zeste homolog 2

Purpose: To investigate the mechanism of microRNA-124 action on neuroblastoma apoptosis induced by sevoflurane. Methods: MiR-124 expression was assessed in a neuroblastoma cell line (SMS-KAN) using quantitative reverse transcription polymerase chain reaction (qRT-PCR). The role of miR-124 in sevoflurane anesthesia-induced neuroblastoma was studied by cell activity and apoptosis analysis using 3-(4, 5-dimethylthiazolyl-2-yl)-2-5 diphenyl tetrazolium bromide (MTT) assay and flow cytometry, respectively. MiR-124 target protein genes were confirmed via luciferase reporter activity, qRT-PCR, and western blot analysis. Results: miR-124 was upregulated in sevoflurane anesthesia-induced neuroblastoma (p < 0.05). After miR-124 knockdown, apoptosis was significantly reduced and cell viability was enhanced in sevoflurane anesthesia-induced SMS-KAN nerve cells (p < 0.05). Furthermore, a significant reduction of luciferase activity was observed in 293T cells co-transfected with miR-124 mimics and EZH2-wild type (EZH2-WT) (p < 0.05). The mRNA and protein expression levels of EZH2 decreased in SMS-KAN nerve cells transfected with miR-124 mimics (p < 0.05). Overexpression of EZH2 inhibited the apoptosis of SMSKAN cells induced by sevoflurane (p < 0.05). Furthermore, the apoptosis of SMS-KAN cells transfected with miR-124 inhibitor were offset by transfected siEZH2. Conclusion: The results suggest that overexpression of miR-124 suppresses cell proliferation by targeting EZH2 in SMS-KAN cells. Therefore, miR-124 represents a potential target for neuroblastoma therap

Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space. Base on matrix factorization applied usually in pattern recognition, LFM models user-item interactions as inner products of factor vectors of user and item in that space and can be efficiently solved by least square methods with optimal estimation. However, such optimal estimation methods are prone to overfitting due to the extreme sparsity of user-item interactions. In this paper, we propose a Bayesian treatment for LFM, named Bayesian Latent Factor Model (BLFM). Based on observed user-item interactions, we build a probabilistic factor model in which the regularization is introduced via placing prior constraint on latent factors, and the likelihood function is established over observations and parameters. Then we draw samples of latent factors from the posterior distribution with Variational Inference (VI) to predict expected value. We further make an extension to BLFM, called BLFMBias, incorporating user-dependent and item-dependent biases into the model for enhancing performance. Extensive experiments on the movie rating dataset show the effectiveness of our proposed models by compared with several strong baselines.Comment: 8 pages, 5 figures, ICPR2020 conferenc

PPCR: Learning Pyramid Pixel Context Recalibration Module for Medical Image Classification

Spatial attention mechanism has been widely incorporated into deep convolutional neural networks (CNNs) via long-range dependency capturing, significantly lifting the performance in computer vision, but it may perform poorly in medical imaging. Unfortunately, existing efforts are often unaware that long-range dependency capturing has limitations in highlighting subtle lesion regions, neglecting to exploit the potential of multi-scale pixel context information to improve the representational capability of CNNs. In this paper, we propose a practical yet lightweight architectural unit, Pyramid Pixel Context Recalibration (PPCR) module, which exploits multi-scale pixel context information to recalibrate pixel position in a pixel-independent manner adaptively. PPCR first designs a cross-channel pyramid pooling to aggregate multi-scale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization, and finally estimates per pixel attention weight via a pixel context integration. PPCR can be flexibly plugged into modern CNNs with negligible overhead. Extensive experiments on five medical image datasets and CIFAR benchmarks empirically demonstrate the superiority and generalization of PPCR over state-of-the-art attention methods. The in-depth analyses explain the inherent behavior of PPCR in the decision-making process, improving the interpretability of CNNs.Comment: 10 page

Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater

Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a Supervised Variational Autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learned a series of latent representations of the soft mechanics transferrable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.Comment: 17 pages, 5 figures, 1 table, submitted to Advanced Intelligent Systems for revie

Optimization of gas-filled quartz capillary discharge waveguide for high-energy laser wakefield acceleration

A hydrogen-filled capillary discharge waveguide made of quartz is presented for high-energy laser wakefield acceleration (LWFA). The experimental parameters (discharge current and gas pressure) were optimized to mitigate ablation by a quantitative analysis of the ablation plasma density inside the hydrogen-filled quartz capillary. The ablation plasma density was obtained by combining a spectroscopic measurement method with a calibrated gas transducer. In order to obtain a controllable plasma density and mitigate the ablation as much as possible, the range of suitable parameters was investigated. The experimental results demonstrated that the ablation in the quartz capillary could be mitigated by increasing the gas pressure to similar to 7.5-14.7 Torr and decreasing the discharge current to similar to 70-100 A. These optimized parameters are promising for future high-energy LWFA experiments based on the quartz capillary discharge waveguide

Ultralow-emittance measurement of high-quality electron beams from a laser wakefield accelerator

By designing a cascaded laser wakefield accelerator, high-quality monoenergetic electron beams (e beams) with peak energies of 340–360MeV and rms divergence of <0.3 mrad were produced. Based on this accelerator, the e-beam betatron radiation spectra were measured exactly via the single-photon counting technique to diagnose the e-beam transverse emittance in a single shot. The e-beam transverse size in the wakefield was estimated to be ~0.35 lm by comparing the measured X-ray spectra with the analytical model of synchrotron-like radiation. By combining the measured e-beam energy and divergence, the normalized transverse emittance was estimated to be as low as 56 um mrad and consistent with particle-in-cell simulations. These high-energy ultralow-emittance e beams hold great potential applications in developing free electron lasers and high-energy X-ray and gamma ray sources

Enhanced betatron radiation by steering a laser-driven plasma wakefield with a tilted shock front

We have experimentally realized a scheme to enhance betatron radiation by manipulating transverse oscillation of electrons in a laser-driven plasma wakefield with a tilted shock front (TSF). Very brilliant betatron x-rays have been produced with significant enhancement both in photon yield and peak energy but almost maintain the e-beam energy spread and charge. Particle-in-cell simulations indicate that the accelerated electron beam (e beam) can acquire a very large transverse oscillation amplitude with an increase in more than 10-fold, after being steered into the deflected wakefield due to the refraction of the driving laser at the TSF. Spectral broadening of betatron radiation can be suppressed owing to the small variation in the peak energy of the low-energy-spread e beam in a plasma wiggler regime. It is demonstrated that the e-beam generation, refracting, and wiggling can act as a whole to realize the concurrence of monoenergetic e beams and bright x-rays in a compact laser-wakefield accelerator