Antitumor effects of a novel photosensitizer-mediated photodynamic therapy and its influence on the cell transcriptome

Photodynamic therapy(PDT)is a promising cancer treatment.This study investigated the antitumor effects and mechanisms of a novel photosensitizer meso-5-[ρ-diethylene triamine pentaacetic acid-aminophenyl]−10,15,20-triphenyl-porphyrin(DTP)mediated PDT(DTP-PDT).Cell viability,reactive oxygen species(ROS),and apoptosis were measured with a Cell Counting Kit-8 assay,DCFH-DA fluorescent probe,and Hoechst staining,respectively.Cell apoptosis-and autophagy-related proteins were examined using western blotting.RNA sequencing was used to screen differentially expressed mRNAs(DERs),and bioinformatic analysis was performed to identify the major biological events after DTP-PDT.Our results show that DTP-PDT inhibited cell growth and induced ROS generation in MCF-7 and SGC7901 cells.The ROS scavenger N-acetyl-L-cysteine(NAC)and the P38 MAPK inhibitor SB203580 alleviated DTP-PDT-induced cytotoxicity.DTP-PDT induced cell apoptosis together with upregulated Bax and downregulated Bcl-2,which could also be inhibited by NAC or SB203580.The level of LC3B-Ⅱ,a marker of autophagy,was increased by DTP-PDT.A total of 3496 DERs were obtained after DTP-PDT.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated that DERs included those involved in cytosolic ribosomes,the nuclear lumen,protein binding,cell cycle,protein targeting to the endoplasmic reticulum,and ribosomal DNA replication.Disease Ontology and Reactome enrichment analyses indicated that DERs were associated with a variety of cancers and cell cycle checkpoints.Protein-protein interaction results demonstrated that cdk1 and rps27a ranked in the top 10 interacting genes.Therefore,DTP-PDT could inhibit cell growth and induce cell apoptosis and autophagy,partly through ROS and the P38 MAPK signaling pathway.Genes associated with the cell cycle,ribosomes,DNA replication,and protein binding may be the key changes in DTP-PDT-mediated cytotoxicity.

A Thermochromic, Viscoelastic Nacre-like Nanocomposite for the Smart Thermal Management of Planar Electronics

Cutting-edge heat spreaders for soft and planar electronics require not only high thermal conductivity and a certain degree of flexibility but also remarkable self-adhesion without thermal interface materials, elasticity, arbitrary elongation along with soft devices, and smart properties involving thermal self-healing, thermochromism and so on. Nacre-like composites with excellent in-plane heat dissipation are ideal as heat spreaders for thin and planar electronics. However, the intrinsically poor viscoelasticity, i.e., adhesion and elasticity, prevents them from simultaneous self-adhesion and arbitrary elongation along with current flexible devices as well as incurring high interfacial thermal impedance. In this paper, we propose a soft thermochromic composite(STC) membrane with a layered structure, considerable stretchability, high in-plane thermal conductivity(~30 Wm^(-1) K^(-1)), low thermal contact resistance(~12 mm^2 KW^(-1), 4–5 times lower than that of silver paste), strong yet sustainable adhesion forces(~4607 Jm^(-2), 2220 Jm^(-2) greater than that of epoxy paste) and self-healing efficiency. As a self-adhesive heat spreader, it implements efficient cooling of various soft electronics with a temperature drop of 20℃ than the polyimide case. In addition to its self-healing function, the chameleon-like behavior of STC facilitates temperature monitoring by the naked eye, hence enabling smart thermal management.

Picture-Induced EEG Signal Classification Based on CVC Emotion Recognition System

Emotion recognition systems are helpful in human-machine interactions and Intelligence Medical applications.Electroencephalogram(EEG)is closely related to the central nervous system activity of the brain.Compared with other signals,EEG is more closely associated with the emotional activity.It is essential to study emotion recognition based on EEG information.In the research of emotion recognition based on EEG,it is a common problem that the results of individual emotion classification vary greatly under the same scheme of emotion recognition,which affects the engineering application of emotion recognition.In order to improve the overall emotion recognition rate of the emotion classification system,we propose the CSP_VAR_CNN(CVC)emotion recognition system,which is based on the convolutional neural network(CNN)algorithm to classify emotions of EEG signals.Firstly,the emotion recognition system using common spatial patterns(CSP)to reduce the EEG data,then the standardized variance(VAR)is selected as the parameter to form the emotion feature vectors.Lastly,a 5-layer CNN model is built to classify the EEG signal.The classification results show that this emotion recognition system can better the overall emotion recognition rate:the variance has been reduced to 0.0067,which is a decrease of 64%compared to that of the CSP_VAR_SVM(CVS)system.On the other hand,the average accuracy reaches 69.84%,which is 0.79%higher than that of the CVS system.It shows that the overall emotion recognition rate of the proposed emotion recognition system is more stable,and its emotion recognition rate is higher.

Emotion Recognition Using WT-SVM in Human-Computer Interaction

With the continuous development of the computer, people's requirements for computers are also getting more and more, so the brain-computer interface system (BCI) has become an essential part of computer research. Emotion recognition is an important task for the computer to understand social status in BCI. Affective computing (AC) aims to develop the model of emotions and advance the affective intelligence of computers. There are various emotion recognition approaches. The method based on electroencephalogram (EEG) is more reliable because it is higher in accuracy and more objective in evaluation than other external appearance clues such as emotion expression and gesture. In this paper, we use the wavelet transform (WT) to extract three kinds of EEG features in time, and frequency domain, which are sub-band energy, energy ratio and root mean square of wavelet coefficients. They reflect the emotion related to EEG activities well. The average classification accuracy of support vector machine (SVM) can reach 82.87%, which indicates that these three features are very effective in emotion recognition. On the other hand, compared with international affective picture system (IAPs), EEG data collected by Chinese affective picture system (CAPs) stimulation has a higher emotion recognition rate, indicating that there are cultural background differences in emotions.

Chromosomal in situ hybridization of Cyprinus carpio genomic DNA repetitive sequence CR1

Common carp Cyprinus carpio genomic DNA repetitive sequence CR1 has been DIG-labeled and hybridized in situ against chromosomes of red common carp ( Cyprinus carpio L. Xingguo red var.). It is found that the repetitive sequence CR1 is mainly localized at the cen-tromeric regions of chromosomes of the red common carp. The application of the

碳纳米管:可控制备决定未来（英文）

在过去几十年中,碳纳米管由于其优异的物理和化学性质而备受关注,被认为是一个强有力的未来明星材料.尽管基于碳纳米管的诸多产品及应用实例相继浮现,但是碳纳米管所呈现出的实际性质与理论值之间依然存在较大差异,无法达到研究者的预期,这源自于目前尚未成熟的控制制备技术.碳纳米管的可控制备技术包括结构的精细控制方法和样品的宏量制备技术,这在很大程度上决定了碳纳米管的未来发展前景.基于此,本文概述了近几十年来研究者们在碳纳米管的精细结构控制、聚合状态设计和样品宏量制备等方面的主要进展,进一步指出了碳纳米管未来的可控制备技术必须与特定化的应用紧密结合,以迎接即将来临的产业化时代.