SLUG is activated by nuclear factor kappa B and confers human alveolar epithelial A549 cells resistance to tumor necrosis factor-alpha-induced apoptosis

BACKGROUND: The role of tumor necrosis factor alpha (TNF-α) in cancer is complex with both apoptotic and anti-apoptotic roles proposed. However the mechanism is not clear. In the study, we designed to investigate the effect of TNF-α on the activation and expression of nuclear factor kappa B (NF-κB)/p65/SLUG/PUMA/Bcl-2 levels in human lung cancer A549 cell line, and in conditions of TNF-α-induced apoptosis. METHODS: We have engineered three A549 cell lines that were transiently transfected with PUMA siRNA, SLUG siRNA and Bcl-2 siRNA, respectively. We have measured the in vitro effects of siRNA on apoptosis, and sensitivity to 20 ng/ml of TNF-α treatment for 24–48 h. RESULTS: We found the NF-κB activity and PUMA mRNA/protein was significantly increased after treatment of TNF-α for 24 h in untreated A549 cells, and led to a significant increase in TNF-α-induced apoptosis, no significant increase of SLUG and Bcl-2 level was shown. However, after treatment of TNF-α for 48 h in untreated A549 cells, SLUG and Bcl-2 level was significant increased, and PUMA level was significant decreased, and TNF-α-induced apoptosis was significantly decreased compared to the apoptosis level after treatment of TNF-α for 24 h. Inhibition of the NF-κB activity could effectively decrease the PUMA level and increase the SLUG and Bcl-2 level. PUMA silencing by siRNA led to a significant decrease in TNF-α-induced apoptosis after treatment of TNF-α for 24 h. Bcl-2 and SLUG silencing by siRNA led to a significant increase in TNF-α-induced apoptosis for 48 h. Furthermore, SLUG silencing increased PUMA level and decreased Bcl-2 level. CONCLUSIONS: The findings suggested that TNF-α treatment promoted apoptosis via the NF-κB-dependent PUMA pathway. The anti-apoptotic role of TNF-α was via NF-κB-dependent SLUG and Bcl-2 pathway at a later time

CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory Accelerators

In recent years, various computing-in-memory (CIM) processors have been presented, showing superior performance over traditional architectures. To unleash the potential of various CIM architectures, such as device precision, crossbar size, and crossbar number, it is necessary to develop compilation tools that are fully aware of the CIM architectural details and implementation diversity. However, due to the lack of architectural support in current popular open-source compiling stacks, existing CIM designs either manually deploy networks or build their own compilers, which is time-consuming and labor-intensive. Although some works expose the specific CIM device programming interfaces to compilers, they are often bound to a fixed CIM architecture, lacking the flexibility to support the CIM architectures with different computing granularity. On the other hand, existing compilation works usually consider the scheduling of limited operation types (such as crossbar-bound matrix-vector multiplication). Unlike conventional processors, CIM accelerators are featured by their diverse architecture, circuit, and device, which cannot be simply abstracted by a single level if we seek to fully explore the advantages brought by CIM. Therefore, we propose CIM-MLC, a universal multi-level compilation framework for general CIM architectures. We first establish a general hardware abstraction for CIM architectures and computing modes to represent various CIM accelerators. Based on the proposed abstraction, CIM-MLC can compile tasks onto a wide range of CIM accelerators having different devices, architectures, and programming interfaces. More importantly, compared with existing compilation work, CIM-MLC can explore the mapping and scheduling strategies across multiple architectural tiers, which form a tractable yet effective design space, to achieve better scheduling and instruction generation results.Comment: 16 pages, 22 figure

The efficacy of mobile phone-based text message interventions (‘Happy Quit’) for smoking cessation in China

Background Considering the extreme shortage of smoking cessation services in China, and the acceptability, feasibility and efficacy of mobile phone-based text message interventions for quitting smoking in other countries, here we propose a study of “the efficacy of mobile phone-based text message interventions (‘Happy Quit’) for smoking cessation in China”. The primary objective of this proposed project is to assess whether a program of widely accessed mobile phone-based text message interventions (‘Happy Quit’) will be effective at helping people in China who smoke, to quit. Based on the efficacy of previous studies in smoking cessation, we hypothesize that ‘Happy Quit’ will be an effective, feasible and affordable smoking cessation program in China. Methods/Design In this single-blind, randomized trial, undertaken in China, about 2000 smokers willing to make a quit attempt will be randomly allocated, using an independent telephone randomization system that includes a minimization algorithm balancing for sex (male, female), age (19–34 or \u3e34 years), educational level (≤ or \u3e12 years), and Fagerstrom score for nicotine addiction (≤5, \u3e5), to ‘Happy Quit’, comprising motivational messages and behavioral-change support, or to a control group that receives text messages unrelated to quitting. Messages will be developed to be suitable for Chinese. A pilot study will be conducted before the intervention to modify the library of messages and interventions. The primary outcome will be self-reported continuous smoking abstinence. A secondary outcome will be point prevalence of abstinence. Abstinence will be assessed at six time points (4, 8, 12, 16, 20 and 24 weeks post-intervention). A third outcome will be reductions in number of cigarettes smoked per day. Discussion/Implications The results will provide valuable insights into bridging the gap between need and services received for smoking cessation interventions and tobacco use prevention in China. It will also serve as mHealth model for extending the public health significance of other interventions, such as mental health interventions

Arrhythmia Classification Algorithm Based on Multi-Feature and Multi-type Optimized SVM

The electrocardiogram (ECG) signal feature extraction and classification diagnosis algorithm is proposed to address the high incidence of heart disease and difficulty in self-detection. First, the collected ECG signals are preprocessed to remove the noise of the ECG signals. Next, wavelet packet decomposition is used to perform a four-layer transformation on the denoised ECG signal and the 16 obtained wavelet packet coefficients analyzed statistically. Next, the slope threshold method is used to extract the R-peak of the denoised ECG signal. The RR interval can be calculated according to the extracted R peak. The extracted statistical features and time domain RR interval features are combined into a multi-domain feature space. Finally, the particle swarm optimization algorithm (PSO), genetic algorithm (GA), and grid search (GS) algorithms are applied to optimize the support vector machine (SVM). The optimized SVM is utilized to classify the extracted multi-domain features. Classification results show the proposed algorithm can classify six types of ECG beats accurately. The classification efficiency achieved by PSO, GA, and GS are 97.78%, 98.33%, and 98.89%, respectively

Study and Design of Diaphragm Pump Vibration Detection Fault Diagnosis System Based on FFT

Abstract: This study has proposed a fault diagnosis system based on vibration detection. The system mainly includes four modules: signal acquisition module, signal processing module, state identification module, fault diagnosis and alarm module. The system uses CMSS 2200 acceleration sensor to collect vibration signals, processing spectrum with FFT (Fast Fourier Transform) which is used effectively in current industry and finally achieve fault diagnosis and prediction for diaphragm pump. Through collection and analysis of the history signal data, set threshold value in the fault diagnosis system. According to the characteristics of different types, set the corresponding effective threshold value. The simulation results show that, the spectrum after FFT transformation processing, can really and effectively reflect equipment operating condition of the diaphragm. This system is not only simple and stable, but also can predict pump failure effectively, so that it reduces equipment downtime, plan maintenance time and unplanned maintenance time

Bilateral Fronto-Parietal Integrity in Young Chronic Cigarette Smokers: A Diffusion Tensor Imaging Study

Cigarette smoking continues to be the leading cause of preventable morbidity and mortality in China and other countries. Previous studies have demonstrated gray matter loss in chronic smokers. However, only a few studies assessed the changes of white matter integrity in this group. Based on those previous reports of alterations in white matter integrity in smokers, the aim of this study was to examine the alteration of white matter integrity in a large, well-matched sample of chronic smokers and non-smokers.Using in vivo diffusion tensor imaging (DTI) to measure the differences of whole-brain white matter integrity between 44 chronic smoking subjects (mean age, 28.0±5.6 years) and 44 healthy age- and sex-matched comparison non-smoking volunteers (mean age, 26.3±5.8 years). DTI was performed on a 3-Tesla Siemens scanner (Allegra; Siemens Medical System). The data revealed that smokers had higher fractional anisotropy (FA) than healthy non-smokers in almost symmetrically bilateral fronto-parietal tracts consisting of a major white matter pathway, the superior longitudinal fasciculus (SLF).We found the almost symmetrically bilateral fronto-parietal whiter matter changes in a relatively large sample of chronic smokers. These findings support the hypothesis that chronic cigarette smoking involves alterations of bilateral fronto-parietal connectivity

ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts

Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and text relevancy. In this paper, we propose ERNIE-ViLG 2.0, a large-scale Chinese text-to-image diffusion model, which progressively upgrades the quality of generated images~by: (1) incorporating fine-grained textual and visual knowledge of key elements in the scene, and (2) utilizing different denoising experts at different denoising stages. With the proposed mechanisms, ERNIE-ViLG 2.0 not only achieves the state-of-the-art on MS-COCO with zero-shot FID score of 6.75, but also significantly outperforms recent models in terms of image fidelity and image-text alignment, with side-by-side human evaluation on the bilingual prompt set ViLG-300