A Significant Increase of RNAi Efficiency in Human Cells by the CMV Enhancer with a tRNAlys Promoter

RNA interference (RNAi) is the process of mRNA degradation induced by double-stranded RNA in a sequence-specific manner. Different types of promoters, such as U6, H1, tRNA, and CMV, have been used to control the inhibitory effect of RNAi expression vectors. In the present study, we constructed two shRNA expression vectors, respectively, controlled by tRNAlys and CMV enhancer-tRNAlys promoters. Compared to the vectors with tRNAlys or U6 promoter, the vector with a CMV enhancer-tRNAlys promoter silenced pokemon more efficiently on both the mRNA and the protein levels. Meanwhile, the silencing of pokemon inhibited the proliferation of MCF7 cells, but the induction of apoptosis of MCF7 cells was not observed. We conclude that the CMV enhancer-tRNAlys promoter may be a powerful tool in driving intracellular expression of shRNA which can efficiently silence targeted gene

AudioInceptionNeXt: TCL AI LAB Submission to EPIC-SOUND Audio-Based-Interaction-Recognition Challenge 2023

This report presents the technical details of our submission to the 2023 Epic-Kitchen EPIC-SOUNDS Audio-Based Interaction Recognition Challenge. The task is to learn the mapping from audio samples to their corresponding action labels. To achieve this goal, we propose a simple yet effective single-stream CNN-based architecture called AudioInceptionNeXt that operates on the time-frequency log-mel-spectrogram of the audio samples. Motivated by the design of the InceptionNeXt, we propose parallel multi-scale depthwise separable convolutional kernels in the AudioInceptionNeXt block, which enable the model to learn the time and frequency information more effectively. The large-scale separable kernels capture the long duration of activities and the global frequency semantic information, while the small-scale separable kernels capture the short duration of activities and local details of frequency information. Our approach achieved 55.43% of top-1 accuracy on the challenge test set, ranked as 1st on the public leaderboard. Codes are available anonymously at https://github.com/StevenLauHKHK/AudioInceptionNeXt.git

P-Glycoprotein/MDR1 Regulates Pokemon Gene Transcription Through p53 Expression in Human Breast Cancer Cells

P-glycoprotein (Pgp), encoded by the multidrug resistance 1 (MDR1) gene, is an efflux transporter and plays an important role in pharmacokinetics. In this study, we demonstrated that the pokemon promoter activity, the pokemon mRNA and protein expression can be significantly inhibited by Pgp. Chromatin immunoprecipitation assay showed that Pgp can bind the pokemon prompter to repress pokemon transcription activity. Furthermore, Pgp regulated pokemon transcription activity through expression of p53 as seen by use of p53 siRNA transfected MCF-7 cells or p53 mutated MDA-MB-231 cells. Moreover, p53 was detected to bind with Pgp in vivo using immunoprecipitation assay. Taken together, we conclude that Pgp can regulate the expression of pokemon through the presence of p53, suggesting that Pgp is a potent regulator and may offer an effective novel target for cancer therapy

Towards Lightweight and Automated Representation Learning System for Networks

We propose LIGHTNE 2.0, a cost-effective, scalable, automated, and high-quality network embedding system that scales to graphs with hundreds of billions of edges on a single machine. In contrast to the mainstream belief that distributed architecture and GPUs are needed for large-scale network embedding with good quality, we prove that we can achieve higher quality, better scalability, lower cost, and faster runtime with shared-memory, CPU-only architecture. LIGHTNE 2.0 combines two theoretically grounded embedding methods NetSMF and ProNE. We introduce the following techniques to network embedding for the first time: (1) a newly proposed downsampling method to reduce the sample complexity of NetSMF while preserving its theoretical advantages; (2) a high-performance parallel graph processing stack GBBS to achieve high memory efficiency and scalability; (3) sparse parallel hash table to aggregate and maintain the matrix sparsifier in memory; (4) a fast randomized singular value decomposition (SVD) enhanced by power iteration and fast orthonormalization to improve vanilla randomized SVD in terms of both efficiency and effectiveness; (5) Intel MKL for proposed fast randomized SVD and spectral propagation; and (6) a fast and lightweight AutoML library FLAML for automated hyperparameter tuning. Experimental results show that LIGHTNE 2.0 can be up to 84X faster than GraphVite, 30X faster than PBG and 9X faster than NetSMF while delivering better performance. LIGHTNE 2.0 can embed very large graph with 1.7 billion nodes and 124 billion edges in half an hour on a CPU server, while other baselines cannot handle very large graphs of this scale

The lipopolysaccharide-triggered mesangial transcriptome: Evaluating the role of interferon regulatory factor-1

The lipopolysaccharide-triggered mesangial transcriptome: Evaluating the role of interferon regulatory factor-1.BackgroundPresently, we do not have a clear picture of how the mesangial transcriptome evolves following stimulation. The present study was designed to address this, using an innate trigger to stimulate murine mesangial cells.MethodsThree independent mesangial cell lines derived from C57BL/6 mice were stimulated with lipopolysaccharide (LPS). The mesangial cell transcriptomes were defined 1, 6, 24, and 60 hours poststimulation with LPS, using a 17,000 gene oligonucleotide array.ResultsInterferon regulatory factor-1 (IRF-1), ScyA2/MCP1, ScyA20/MIP3α (ScyB1/Gro1, and ScyB2/MIP2α/Gro2 were the earliest genes to be hyperexpressed after LPS stimulation. Later-appearing genes included ScyA7/MCP3, ScyD1/fractalkine, GM-CSF/CSF-2, PDGF, epiregulin, NfKb, C/EBP, TIMP-1, MMP11, MMP13, PTGS2/COX2, SpI2-1, Spp1, PAI-1, VCAM-1, C3, and defensin-β1, among others. Several of these changes were validated by real-time polymerase chain reaction (PCR) or enzyme-linked immunosorbent assay (ELISA). Rapid IRF-1 hyperexpression was also noted following stimulation of mesangial cells with peptidoglycan, poly I:poly C, interferon-γ?(IFN-γ), and heat-aggregated IgG. However, the blocking of IRF-1 using RNA interference and the use of mesangial cells isolated from IRF-1–deficient mice could not substantiate an obligatory role for IRF-1 in LPS-induced mesangial cell activation. Likewise, IRF-1 deficiency did not impact the development of anti-glomerular basement membrane (GBM)-induced immune nephritis.ConclusionInnate stimuli such as LPS appear to trigger successive waves of mesangial cell gene expression. Although IRF-1 surfaces as an “early-on, early-off” transcription factor following several different triggers, it does not appear to be an essential molecule for mesangial cell activation by innate triggers or for anti-GBM disease

2-Chloro-1-[4-(2-fluoro­benz­yl)piperazin-1-yl]ethanone

In the title compound, C13H16ClFN2O, the piperazine ring is flanked by 1-(2-fluoro­benz­yl)piperazine and adopts a chair conformation. The dihedral angle between the fluoro­phenyl ring and the four planar C atoms (r.m.s. = 0.0055 Å) of the piperazine chair is 78.27 (7)°, whereas the dihedral angle between the four planar C atoms of the piperazine chair and the ethanone plane is 55.21 (9) Å; the Cl atom displaced by1.589 (2) Å out of the plane

Deep reinforcement learning-aided autonomous navigation with landmark generators

Mobile robots are playing an increasingly significant role in social life and industrial production, such as searching and rescuing robots, autonomous exploration of sweeping robots, and so on. Improving the accuracy of autonomous navigation of mobile robots is a hot issue to be solved. However, traditional navigation methods are unable to realize crash-free navigation in an environment with dynamic obstacles, more and more scholars are gradually using autonomous navigation based on deep reinforcement learning (DRL) to replace overly conservative traditional methods. But on the other hand, DRL's training time is too long, and the lack of long-term memory easily leads the robot to a dead end, which makes its application in the actual scene more difficult. To shorten training time and prevent mobile robots from getting stuck and spinning around, we design a new robot autonomous navigation framework which combines the traditional global planning and the local planning based on DRL. Therefore, the entire navigation process can be transformed into first using traditional navigation algorithms to find the global path, then searching for several high-value landmarks on the global path, and then using the DRL algorithm to move the mobile robot toward the designated landmarks to complete the final navigation, which makes the robot training difficulty greatly reduced. Furthermore, in order to improve the lack of long-term memory in deep reinforcement learning, we design a feature extraction network containing memory modules to preserve the long-term dependence of input features. Through comparing our methods with traditional navigation methods and reinforcement learning based on end-to-end depth navigation methods, it shows that while the number of dynamic obstacles is large and obstacles are rapidly moving, our proposed method is, on average, 20% better than the second ranked method in navigation efficiency (navigation time and navigation paths' length), 34% better than the second ranked method in safety (collision times), 26.6% higher than the second ranked method in success rate, and shows strong robustness