Inhibition of HIV derived lentiviral production by TAR RNA binding domain of TAT protein

BACKGROUND: A critical step in the production of new HIV virions involves the TAT protein binding to the TAR element. The TAT protein contains in close proximity its TAR RNA binding domain and protein transduction domain (PTD). The PTD domain of TAT has been identified as being instrumental in the protein's ability to cross mammalian cell and nuclear membranes. All together, this information led us to form the hypothesis that a protein containing the TAR RNA binding domain could compete with the native full length TAT protein and effectively block the TAR RNA binding site in transduced HIV infected cells. RESULTS: We synthesized a short peptide named Tat-P, which contained the TAR RNA binding and PTD domains to examine whether the peptide has the potential of inhibiting TAT dependent HIV replication. We investigated the inhibiting effects of Tat-P in vitro using a HIV derived lentiviral vector model. We found that the TAT PTD domain not only efficiently transduced test cells, but also effectively inhibited the production of lentiviral particles in a TAT dependent manner. These results were also supported by data derived from the TAT activated LTR-luciferase expression model and RNA binding assays. CONCLUSION: Tat-P may become part of a category of anti-HIV drugs that competes with full length TAT proteins to inhibit HIV replication. In addition, this study indicates that the HIV derived lentiviral vector system is a safe and reliable screening method for anti-HIV drugs, especially for those targeting the interaction of TAT and TAR RNAs

DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations

Language models pre-trained on general text have achieved impressive results in diverse fields. Yet, the distinct linguistic characteristics of task-oriented dialogues (TOD) compared to general text limit the practical utility of existing language models. Current task-oriented dialogue pre-training methods overlook the one-to-many property of conversations, where multiple responses can be appropriate given the same conversation context. In this paper, we propose a novel dialogue pre-training model called DivTOD, which collaborates with LLMs to learn diverse task-oriented dialogue representations. DivTOD guides LLMs in transferring diverse knowledge to smaller models while removing domain knowledge that contradicts task-oriented dialogues. Experiments show that our model outperforms strong TOD baselines on various downstream dialogue tasks and learns the intrinsic diversity of task-oriented dialogues.Comment: NAACL 2024 (Findings

Hierarchical speaker representation for target speaker extraction

Target speaker extraction aims to isolate a specific speaker's voice from a composite of multiple sound sources, guided by an enrollment utterance or called anchor. Current methods predominantly derive speaker embeddings from the anchor and integrate them into the separation network to separate the voice of the target speaker. However, the representation of the speaker embedding is too simplistic, often being merely a 1*1024 vector. This dense information makes it difficult for the separation network to harness effectively. To address this limitation, we introduce a pioneering methodology called Hierarchical Representation (HR) that seamlessly fuses anchor data across granular and overarching 5 layers of the separation network, enhancing the precision of target extraction. HR amplifies the efficacy of anchors to improve target speaker isolation. On the Libri-2talker dataset, HR substantially outperforms state-of-the-art time-frequency domain techniques. Further demonstrating HR's capabilities, we achieved first place in the prestigious ICASSP 2023 Deep Noise Suppression Challenge. The proposed HR methodology shows great promise for advancing target speaker extraction through enhanced anchor utilization.Comment: Accepted to ICASSP 202

MC-SpEx: Towards Effective Speaker Extraction with Multi-Scale Interfusion and Conditional Speaker Modulation

The previous SpEx+ has yielded outstanding performance in speaker extraction and attracted much attention. However, it still encounters inadequate utilization of multi-scale information and speaker embedding. To this end, this paper proposes a new effective speaker extraction system with multi-scale interfusion and conditional speaker modulation (ConSM), which is called MC-SpEx. First of all, we design the weight-share multi-scale fusers (ScaleFusers) for efficiently leveraging multi-scale information as well as ensuring consistency of the model's feature space. Then, to consider different scale information while generating masks, the multi-scale interactive mask generator (ScaleInterMG) is presented. Moreover, we introduce ConSM module to fully exploit speaker embedding in the speech extractor. Experimental results on the Libri2Mix dataset demonstrate the effectiveness of our improvements and the state-of-the-art performance of our proposed MC-SpEx.Comment: Accepted by InterSpeech 202

TEA-PSE 3.0: Tencent-Ethereal-Audio-Lab Personalized Speech Enhancement System For ICASSP 2023 DNS Challenge

This paper introduces the Unbeatable Team's submission to the ICASSP 2023 Deep Noise Suppression (DNS) Challenge. We expand our previous work, TEA-PSE, to its upgraded version -- TEA-PSE 3.0. Specifically, TEA-PSE 3.0 incorporates a residual LSTM after squeezed temporal convolution network (S-TCN) to enhance sequence modeling capabilities. Additionally, the local-global representation (LGR) structure is introduced to boost speaker information extraction, and multi-STFT resolution loss is used to effectively capture the time-frequency characteristics of the speech signals. Moreover, retraining methods are employed based on the freeze training strategy to fine-tune the system. According to the official results, TEA-PSE 3.0 ranks 1st in both ICASSP 2023 DNS-Challenge track 1 and track 2.Comment: Accepted by ICASSP 202

Inhibitory effects of diarsenic trioxide (As2O3) on hepatocellular carcinoma cells exerted by regulation of promyelocytic leukemia protein levels

Previous Chinese research revealed that diarsenic trioxide (As2O3) inhibits acute promyelocytic leukemia (PML) cell proliferation and initiates apoptosis through degradation of the PML-retinoic acid receptor protein. This study was to analyse whether As2O3 also had an effect on hepatocellular carcinoma (HCC) cells. As2O3 effects on various HCC cell lines and primary HCC cells were investigated in time and dose series, including measurements of cell growth, PML mRNA and protein expression, xenografted tumor formation, and the self-renewal Oct4 and hepatocyte marker expressions in mouse model xenografts or cells treated with PML siRNA. The results were analyzed by immunocytochemistry, quantitative reverse transcription PCR and western blotting as well as indocyanine green and Periodic Acid Schiff staining. As2O3 inhibited HCC cell and HCC cell-derived xenograft tumor formation in a time-dependent manner and reduced PML protein expression in HCC cells, but had limited effects on PML mRNA levels in cell nuclei. The HCC cell line HuH7 treated with As2O3 showed a decreased expression of alpha-fetoprotein and increased expression and transcription of mature hepatocyte markers, indicating differentiation of HCC cells into hepatocytes. Cytokeratin 18 protein and mRNA levels as well as tyrosine aminotransferase and apolipoprotein B mRNA transcriptions were enhanced by As2O3 as were the numbers of indocyanine green and Periodic Acid Schiff stained cells. In addition, As2O3 downregulated the expression of Oct4. In conclusion, since As2O3 inhibited HCC cell proliferation and HCC cell-derived xenograft tumor formation it is suggested that an appropriate concentration of As2O3 might be a promising therapy to treat HCC

Epitope-optimization creates highly immunogenic alpha fetoprotein antigen to break immune tolerance and potently activates CD8 T cells to prevents autochthonous hepatocellular carcinoma

In this study, we investigated whether mouse alpha fetoprotein (mAFP), the shared self/tumor antigen of hepatocellular carcinoma (HCC), could be rationally engineered to create effective vaccine to break tolerance and potently activate CD8 T cells to prevent clinically-relevant carcinogen-induced autochthonous HCC. We found that the computer-guided epitope-optimization created optimized opt-mAFP and that immunization with lentivector (lv) expressing opt-mAFP, but not wt-mAFP, potently activated CD8 cells specific for three novel H-2b restricted CD8 epitopes, which cross-recognized wt-mAFP epitopes naturally processed and presented by wt-mAFP+ tumor cells. Immunization with opt-mAFP-lv, but not wt-mAFP-lv, completely protected mice from wt-mAFP+ tumor challenge and effectively prevented carcinogen-induced autochthonous HCC. Prime-boost with opt-mAFP-lv and vaccinia vector opt-mAFP-vv significantly increased the wt-mAFP-specific CD8 T cells that were highly responsive to emerging HCC tumor cells in the liver, enhancing prevention of autochthonous HCC. Our data demonstrate that epitope-optimization creates immunogenic opt-mAFP that is able to break tolerance and activate potent CD8 responses, which can cross-recognize wt-mAFP peptides, but also recognize and kill mAFP+ tumor cells. Our study provides a practical roadmap to develop effective human vaccines that should have a better chance of success than the current human HCC vaccines based on native wt-AFP