Research on the Interaction between Tubeimoside 1 and HepG2 Cells Using the Microscopic Imaging and Fluorescent Spectra Method

The treatment of cancer draws interest from researchers worldwide. Of the different extracts from traditional Chinese medicines, Tubeimoside 1 (TBMS 1) is regarded as an effective treatment for cancer. To determine the mechanism of TBMS 1, the shape/pattern of HepG2 cells based on the microscopic imaging technology was determined to analyze experimental results; then the fluorescent spectra method was designed to investigate whether TBMS 1 affected HepG2 cells. A three-dimensional (3D) fluorescent spectra sweep was performed to determine the characteristic wave peak of HepG2 cells. A 2D fluorescent spectra method was then used to show the florescence change in HepG2 cells following treatment with TBMS 1. Finally, flow cytometry was employed to analyze the cell cycle of HepG2 cells. It was shown that TBMS 1 accelerated the death of HepG2 cells and had a strong dose- and time-dependent growth inhibitory effect on HepG2 cells, especially at the G2/M phase. These results indicate that the fluorescent spectra method is a promising substitute for flow cytometry as it is rapid and cost-effective in HepG2 cells

A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models

With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, most of the current works perform the attack in a white-box fashion: they need to access the model predictions and labels to construct their adversarial loss. However, the inaccessibility of model predictions in real systems makes the white-box attack impractical to real graph learning system. This paper promotes current frameworks in a more general and flexible sense -- we demand to attack various kinds of graph embedding model with black-box driven. To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter. As such, a generalized adversarial attacker: GF-Attack is constructed by the graph filter and feature matrix. Instead of accessing any knowledge of the target classifiers used in graph embedding, GF-Attack performs the attack only on the graph filter in a black-box attack fashion. To validate the generalization of GF-Attack, we construct the attacker on four popular graph embedding models. Extensive experimental results validate the effectiveness of our attacker on several benchmark datasets. Particularly by using our attack, even small graph perturbations like one-edge flip is able to consistently make a strong attack in performance to different graph embedding models.Comment: Accepted by the AAAI 202

Thin film aluminum nitride surface acoustic wave resonators for quantum acoustodynamics

The quantum excitations of macroscopic surface acoustic waves (SAWs) have been tailored to control, communicate and transduce stationary and flying quantum states. However, the limited lifetime of this hybrid quantum systems remains critical obstacles to extend their applications in quantum information processing. Here we present the potentials of thin film aluminum nitride to on-chip integrate phonons with superconducting qubits over previous bulk piezoelectric substrates. We have reported high-quality thin film GHz-SAW resonators with the highest internal quality factor Qi of 5 e4 at the single-phonon level. The internal loss of SAW resonators are systematically investigated with tuning the parameters of sample layout, power and temperature. Our results manifest that SAWs on piezoelectric films are readily integrable with standard fabrication of Josephson junction quantum circuits, and offer excellent acoustic platforms for the high-coherence quantum acoustodynamics architectures.Comment: 10 pages, 4 figure

An adaptive ensemble approach to ambient intelligence assisted people search

Some machine learning algorithms have shown a better overall recognition rate for facial recognition than humans, provided that the models are trained with massive image databases of human faces. However, it is still a challenge to use existing algorithms to perform localized people search tasks where the recognition must be done in real time, and where only a small face database is accessible. A localized people search is essential to enable robot–human interactions. In this article, we propose a novel adaptive ensemble approach to improve facial recognition rates while maintaining low computational costs, by combining lightweight local binary classifiers with global pre-trained binary classifiers. In this approach, the robot is placed in an ambient intelligence environment that makes it aware of local context changes. Our method addresses the extreme unbalance of false positive results when it is used in local dataset classifications. Furthermore, it reduces the errors caused by affine deformation in face frontalization, and by poor camera focus. Our approach shows a higher recognition rate compared to a pre-trained global classifier using a benchmark database under various resolution images, and demonstrates good efficacy in real-time tasks

The Role of EjSOC1s in Flower Initiation in Eriobotrya japonica

The MADS-box transcription factor SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1) integrates environmental and endogenous signals to promote flowering in Arabidopsis. However, the role of SOC1 homologs in regulating flowering time in fruit trees remains unclear. To better understand the molecular mechanism of flowering regulation in loquat (Eriobotrya japonica Lindl.), two SOC1 homologs (EjSOC1-1 and EjSOC1-2) were identified and characterized in this work. Sequence analysis showed that EjSOC1-1 and EjSOC1-2 have conserved MADS-box and K-box domains. EjSOC1-1 and EjSOC1-2 were clearly expressed in vegetative organs, and high expression was detected in flower buds. As observed in paraffin-embedded sections, expression of the downstream flowering genes EjAP1s and EjLFYs started to increase at the end of June, a time when flower bud differentiation occurs. Additionally, high expression of EjSOC1-1 and EjSOC1-2 began 10 days earlier than that of EjAP1s and EjLFYs in shoot apical meristem (SAM). EjSOC1-1 and EjSOC1-2 were inhibited by short-day (SD) conditions and exogenous GA3, and flower bud differentiation did not occur after these treatments. EjSOC1-1 and EjSOC1-2 were found to be localized to the nucleus. Moreover, ectopic overexpression of EjSOC1-1 and EjSOC1-2 in wild-type Arabidopsis promoted early flowering, and overexpression of both was able to rescue the late flowering phenotype of the soc1-2 mutant. In conclusion, the results suggest that cultivated loquat flower bud differentiation in southern China begins in late June to early July and that EjSOC1-1 and EjSOC1-2 participate in the induction of flower initiation. These findings provide new insight into the artificial regulation of flowering time in fruit trees