Cationic chitosan derivatives as potential antifungals: A review of structural optimization and applications

The increasing resistance of pathogen fungi poses a global public concern. There are several limitations in current antifungals, including few available fungicides, severe toxicity of some fungicides, and drug resistance. Therefore, there is an urgent need to develop new antifungals with novel targets. Chitosan has been recognized as a potential antifungal substance due to its good biocompatibility, biodegradability, non-toxicity, and availability in abundance, but its applications are hampered by the low charge density results in low solubility at physiological pH. It is believed that enhancing the positive charge density of chitosan may be the most effective approach to improve both its solubility and antifungal activity. Hence, this review mainly focuses on the structural optimization strategy of cationic chitosan and the potential antifungal applications. This review also assesses and comments on the challenges, shortcomings, and prospect of cationic chitosan derivatives as antifungal therapy

Multi-GradSpeech: Towards Diffusion-based Multi-Speaker Text-to-speech Using Consistent Diffusion Models

Recent advancements in diffusion-based acoustic models have revolutionized data-sufficient single-speaker Text-to-Speech (TTS) approaches, with Grad-TTS being a prime example. However, diffusion models suffer from drift in training and sampling distributions due to imperfect score-matching. The sampling drift problem leads to these approaches struggling in multi-speaker scenarios in practice. In this paper, we present Multi-GradSpeech, a multi-speaker diffusion-based acoustic models which introduces the Consistent Diffusion Model (CDM) as a generative modeling approach. We enforce the consistency property of CDM during the training process to alleviate the sampling drift problem in the inference stage, resulting in significant improvements in multi-speaker TTS performance. Our experimental results corroborate that our proposed approach can improve the performance of different speakers involved in multi-speaker TTS compared to Grad-TTS, even outperforming the fine-tuning approach. Audio samples are available at https://welkinyang.github.io/multi-gradspeech

Pseudo-Siamese Network based Timbre-reserved Black-box Adversarial Attack in Speaker Identification

In this study, we propose a timbre-reserved adversarial attack approach for speaker identification (SID) to not only exploit the weakness of the SID model but also preserve the timbre of the target speaker in a black-box attack setting. Particularly, we generate timbre-reserved fake audio by adding an adversarial constraint during the training of the voice conversion model. Then, we leverage a pseudo-Siamese network architecture to learn from the black-box SID model constraining both intrinsic similarity and structural similarity simultaneously. The intrinsic similarity loss is to learn an intrinsic invariance, while the structural similarity loss is to ensure that the substitute SID model shares a similar decision boundary to the fixed black-box SID model. The substitute model can be used as a proxy to generate timbre-reserved fake audio for attacking. Experimental results on the Audio Deepfake Detection (ADD) challenge dataset indicate that the attack success rate of our proposed approach yields up to 60.58% and 55.38% in the white-box and black-box scenarios, respectively, and can deceive both human beings and machines.Comment: 5 page