Teleportation of an arbitrary two-particle state via entanglement swapping

A scheme of teleportation of an arbitrary two-particle state is presented when two pairs of entangled particles are used as quantum channels. After the Bell state measurements are operated by the sender, the original state with deterministic probability can be reconstructed by the receiver when a corresponding unitary transformation is followed

Reactive Oxygen Species and p38 Mitogen-activated Protein Kinase Mediate Exercise-induced Skeletal Muscle-derived Interleukin-6 Expression

Interleukin-6 (IL-6) is a pleiotropic cytokine secreted by many different cell types, and skeletal muscle is an important source of IL-6 during exercise. Here, we studied the effects of glucose deprivation in vitro on skeletal muscle-derived IL-6 expression and release in C2C12 myocytes, as well as its regulation by p38 mitogen-activated protein kinase (p38MAPK) and reactive oxygen species (ROS). C2C12 myotubes were cultured in DMEM medium containing 4.5 g · L−1 glucose (glucose control, GC) or DMEM medium containing no glucose (glucose deprivation, GD) for 0, 6, 12, 18 and 24 hours, and then incubated with 10mM NAC (a ROS scavenger) or 10 μM SB203580 (a p38MAPK inhibitor) under either GC or GD conditions for 24 hours. IL-6 expression levels were subsequently analyzed using RT–PCR, and IL-6 protein levels in the medium were measured using ELISA. Glucose deprivation significantly enhanced IL-6 expression at 18 and 24 hours compared to the glucose control, and caused IL-6 protein levels to increase significantly over the entire 24-hour measurement period. The ROS scavenger NAC inhibited the glucose deprivation-induced release of IL-6 protein almost completely, while the p38MAPK inhibitor SB203580 inhibited glucose deprivation-induced IL-6 protein release to a lesser extent. Our study suggests that glucose deprivation in C2C12 myocytes induces IL-6 expression and release, and that this IL-6 release is mainly mediated via ROS signaling. Skeletal muscle-derived IL-6 may thus play an important role in energy metabolism during exercise

Correlation between Internet Addiction Disorder and Mental Health of Junior Middle School Students in Chengdu

Objective: To study the prevalence and main influencing factors of Internet addiction among junior middle school students in Chengdu, and to provide scientific basis for the prevention and intervention of Internet addiction. Methods: From September to December 2017, 3,607 junior middle school students were randomly selected from 8 middle schools in Chengdu by stratified cluster sampling, and investigated by selfmade questionnaire. SPSS 19.0 software was used for χ 2 test and multiple linear regression analysis. Results: 174 of 3,607 junior middle school students in Chengdu were diagnosed with Internet addiction, and the detection rate of Internet addiction was 4.8%. There were significant differences in the scores of mental health, learning pressure, parent-child relationship and peer relationship between Internet addiction and non-internet addiction junior middle school students (P < 0.05). The results of multiple linear regression showed that family economic status, learning pressure, depression and anxiety were positively correlated with internet addiction, while parent-child relationship, peer relationship and social support were negatively correlated with Internet addiction (P < 0.05, P < 0.01). Conclusion: The detection rate of Internet addiction among junior middle school students in Chengdu is at a low level. Junior middle school students with low social support and high depression and anxiety have a higher risk of Internet addiction, which can be reduced by improving their mental health

DiNuP: a systematic approach to identify regions of differential nucleosome positioning

Motivation: With the rapid development of high-throughput sequencing technologies, the genome-wide profiling of nucleosome positioning has become increasingly affordable. Many future studies will investigate the dynamic behaviour of nucleosome positioning in cells that have different states or that are exposed to different conditions. However, a robust method to effectively identify the regions of differential nucleosome positioning (RDNPs) has not been previously available. Results:: We describe a novel computational approach, DiNuP, that compares nucleosome profiles generated by high-throughput sequencing under various conditions. DiNuP provides a statistical P-value for each identified RDNP based on the difference of read distributions. DiNuP also empirically estimates the false discovery rate as a cutoff when two samples have different sequencing depths and differentiate reliable RDNPs from the background noise. Evaluation of DiNuP showed it to be both sensitive and specific for the detection of changes in nucleosome location, occupancy and fuzziness. RDNPs that were identified using publicly available datasets revealed that nucleosome positioning dynamics are closely related to the epigenetic regulation of transcription. Availability and implementation: DiNuP is implemented in Python and is freely available at http://www.tongji.edu.cn/~zhanglab/DiNuP

Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference

Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of Multiplication-Free Inference (MFI) to harmonize with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposes limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in Multi-Layer Perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization to retain MFI compatibility and introduces a spiking patch encoding layer to reinforce local feature extraction capabilities. As a result, we establish an efficient multi-stage spiking MLP network that effectively blends global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pre-training or sophisticated SNN training techniques, our network secures a top-1 accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model capacity, and simulation steps. An expanded version of our network challenges the performance of the spiking VGG-16 network with a 71.64% top-1 accuracy, all while operating with a model capacity 2.1 times smaller. Our findings accentuate the potential of our deep SNN architecture in seamlessly integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells.Comment: 11 pages, 6 figure

Local chromatin dynamics of transcription factors imply cell-lineage specific functions during cellular differentiation

Chromatin dynamics across cellular differentiation states is an emerging perspective from which the mechanism of global gene expression regulation may be better understood. While the roles of some histone marks have been partially interpreted in terms of their association with gene transcription, the dynamics of histone marks from a loci-specific perspective during cellular differentiation is not well studied. We established a method to systematically assess the histone modification variations of genes across various cellular differentiation states. We calculated the histone modification variation scores of H3K4me3, H3K27me3 and H3K36me3 for over 1300 curated transcription factors (TFs) during human blood cell differentiation. Hematopoietic-specific TFs (identified by literature mining) were significantly overrepresented by TFs with higher histone modification variation scores. Hierarchical clustering of all TFs based on the histone modification variation scores defined a group of TFs where known or potential hematopoietic-specific TFs were remarkably enriched. Our results suggest that local chromatin state dynamics of transcription factors across cellular differentiation states could imply cell lineage-specific functions. More importantly, our method can be applied to broader systems, holding the promise to discover de novo, lineage-specific TFs by interrogating their histone modification dynamics across cell lineages

Analysis of dynamic stability for wind turbine blade under fluid-structure interaction

Aiming at improving vibration performance of 1.5 MW wind turbine blades, the theoretical model and the calculation process of vibration problems under geometric nonlinearity and unidirectional fluid-structure interaction (UFSI) were presented. The dynamic stability analysis on a 1.5 MW wind turbine blade was carried out. Both the maximum brandish displacement and the maximum Mises stress increase nonlinearly with the increase of wind speed. The influences of turbulent effect, wind shear effect and their joint effect on displacement and stress increase sequentially. Furthermore, the stability critical curves are calculated and analyzed. As a result, the stability region is established where the wind turbine blade can run safely