The Forkhead box transcription factor FOXM1 is required for the maintenance of cell proliferation and protection against oxidative stress in human embryonic stem cells

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The Basic Helix-Loop-Helix Transcription Factor Family in the Honey Bee, Apis mellifera

The basic helix-loop-helix (bHLH) transcription factors play important roles in a wide range of developmental processes in higher organisms. bHLH family members have been identified in a dozen of organisms including fruit fly, mouse and human. In this study, we identified 51 bHLH sequences in silico in the honey bee, Apis mellifera L. (Hymenoptera: Apidae), genome. Phylogenetic analyses revealed that they belong to 38 bHLH families with 21, 11, 9, 1, 8 and 1 members in high-order groups A, B, C, D, E and F, respectively. Using phylogenetic analyses, all of the 51 bHLH sequences were assigned to their corresponding families. Genes that encode ASCb, NeuroD, Oligo, Delilah, MyoRb, Figa and Mad were not found in the honey bee genome. The present study provides useful background information for future studies using the honey bee as a model system for insect development

TGFBI promoter hypermethylation correlating with paclitaxel chemoresistance in ovarian cancer

The purpose of this study is to determine the methylation status of Transforming growth factor-beta-inducible gene-h3 (TGFBI) and its correlation with paclitaxel chemoresistance in ovarian cancer. The methylation status of TGFBI was examined in ovarian cancer and control groups by methylation-specific PCR (MSP) and bisulfite sequencing PCR (BSP). The TGFBI expression and cell viability were compared by Quantitative Real-Time PCR, Western Blotting and MTT assay before and after demethylating agent 5-aza-2'-deoxycytidine (5-aza-dc) treatment in 6 cell lines (SKOV3, SKOV3/TR, SKOV3/DDP, A2780, 2780/TR, OVCAR8). In our results, TGFBI methylation was detected in 29/40 (72.5%) of ovarian cancer and 1/10 (10%) of benign ovarian tumors. No methylation was detected in normal ovarian tissues (P < 0.001). No statistical correlation between RUNX3 methylation and clinicopathological characteristics was observed. A significant correlation between TGFBI methylation and loss of TGFBI mRNA expression was found (P < 0.001). The methylation level of TGFBI was significantly higher in paclitaxel resistant cell lines (SKOV3/TR and 2780/TR) than that in the sensitive pairs (P < 0.001). After 5-aza-dc treatment, the relative expression of TGFBI mRNA and protein increased significantly in SKOV3/TR and A2780/TR cells. However, no statistical differences of relative TGFBI mRNA expression and protein were found in other cells (all P > 0.05), which showed that re-expression of TGFBI could reverse paclitaxel chemoresistance. Our results show that TGFBI is frequently methylated and associated with paclitaxel-resistance in ovarian cancer. TGFBI might be a potential therapeutic target for the enhancement of responses to chemotherapy in ovarian cancer patients

A Genome-Wide Survey on Basic Helix-Loop-Helix Transcription Factors in Giant Panda

The giant panda (Ailuropoda melanoleuca) is a critically endangered mammalian species. Studies on functions of regulatory proteins involved in developmental processes would facilitate understanding of specific behavior in giant panda. The basic helix-loop-helix (bHLH) proteins play essential roles in a wide range of developmental processes in higher organisms. bHLH family members have been identified in over 20 organisms, including fruit fly, zebrafish, mouse and human. Our present study identified 107 bHLH family members being encoded in giant panda genome. Phylogenetic analyses revealed that they belong to 44 bHLH families with 46, 25, 15, 4, 11 and 3 members in group A, B, C, D, E and F, respectively, while the remaining 3 members were assigned into “orphan”. Compared to mouse, the giant panda does not encode seven bHLH proteins namely Beta3a, Mesp2, Sclerax, S-Myc, Hes5 (or Hes6), EBF4 and Orphan 1. These results provide useful background information for future studies on structure and function of bHLH proteins in the regulation of giant panda development

The First Protocol Of Reaching Consensus Under Unreliable Mobile Edge Computing Paradigm

Mobile Edge Computing (MEC) is an emerging technology that enables computing directly at the edge of the cloud computing network. Therefore, it is important that MEC is applied with reliable transmission. The problem of reaching consensus in the distributed system is one of the most important issues in designing a reliable transmission network. However, all previous protocols for the consensus problem are not suitable for an MEC paradigm. It is the first time an optimal protocol of reaching consensus is pro- posed for MEC paradigm. The protocol makes all fault-free nodes communicate with each other and collect the exchanged messages to decide a common value. Based on the common value, the protocol ensures all fault-free nodes reach consensus without the influence of unreliable transmission. Finally, we proved theoretically that the proposed protocol can tolerate the maximum number of faulty components and using only two rounds of message exchanges

Superresolution Reconstruction of Single Image for Latent features

In recent years, Deep Learning has shown good results in the Single Image Superresolution Reconstruction (SISR) task, thus becoming the most widely used methods in this field. The SISR task is a typical task to solve an uncertainty problem. Therefore, it is often challenging to meet the requirements of High-quality sampling, fast Sampling, and diversity of details and texture after Sampling simultaneously in a SISR task.It leads to model collapse, lack of details and texture features after Sampling, and too long Sampling time in High Resolution (HR) image reconstruction methods. This paper proposes a Diffusion Probability model for Latent features (LDDPM) to solve these problems. Firstly, a Conditional Encoder is designed to effectively encode Low-Resolution (LR) images, thereby reducing the solution space of reconstructed images to improve the performance of reconstructed images. Then, the Normalized Flow and Multi-modal adversarial training are used to model the denoising distribution with complex Multi-modal distribution so that the Generative Modeling ability of the model can be improved with a small number of Sampling steps. Experimental results on mainstream datasets demonstrate that our proposed model reconstructs more realistic HR images and obtains better PSNR and SSIM performance compared to existing SISR tasks, thus providing a new idea for SISR tasks