לוח שנה משולב

Circadian clocks in unicellular phototrophic organisms are known to display remarkable reliability. In contrast, not much is known about how circadian clocks perform in a multicellular setting. Are clocks in multicellular cyanobacteria coupled and synchronized with one another? Are clocks entrained only by external cues? What is the spatial extent of synchronization? What is the role of cell-cell variations in copy numbers of molecules comprising the core clock (demographic noise) in setting the temporal pattern and its robustness? To tackle quantitatively these and other questions, we studied the dynamics of a circadian clock-controlled gene in Anabaena sp. PCC 7120, a multicellular cyanobacterium in which cells are arranged one after the other and coupled by protein channels, in a one-dimensional structure. Our real-time, single-cell level measurements showed significant synchronization and spatial coherence along filaments, and clock coupling mediated by cell-cell communication. Furthermore, we found significant variability in expression between different cells along filaments. A stochastic one-dimensional toy model of coupled clocks and their phosphorylation states shows that demographic noise can seed stochastic oscillations outside the region where deterministic limit cycles with circadian periods occur. The model reproduces the observed spatio-temporal coherence along filaments and provides a robust description of coupled circadian clocks in a multicellular organism, despite significant stochasticity in biomolecular reactions. Lastly, we carried out experiments in which developmental processes were induced. Our experiments showed that gene expression in different vegetative intervals along a developed filament was discoordinated, and that differentiation took place preferentially within a limited interval of the circadian clock cycle. The transition to multicellularity demanded coordination between clocks via cell-cell communication, to optimize fitness in the presence of significant demographic noise.

Lip-to-speech involves generating a natural-sounding speech synchronized with a soundless video of a person talking. Despite recent advances, current methods still cannot produce high-quality speech with high levels of intelligibility for challenging and realistic datasets. This talk presents LipVoicer, a novel method that generates high-quality speech, even for in-the-wild and rich datasets, by incorporating the text modality. Given a silent video, we first predict the spoken text using a pre-trained lip-reading network. We then condition a diffusion model on the video and use the extracted text through a classifier-guidance mechanism where a pre-trained automatic speech recognition (ASR) serves as the classifier. We demonstrate the effectiveness of LipVoicer through human evaluation, which shows that it produces more natural and synchronized speech signals than competing methods (demo page: https://lipvoicer.github.io). The presented LipVoicer is a joint work of Yochai Yemini, Aviv Shamsian, Lior Bracha, Sharon Gannot, and Ethan Fetaya.

Bio:

Sharon Gannot obtained his Ph.D. in electrical engineering from Tel-Aviv University, Israel, in 2000. He is a full professor in the Faculty of Engineering at Bar-Ilan University, Israel. He serves as a senior area chair for IEEE Transactions on Audio, Speech, and Language Processing, a member of the senior editorial board of IEEE Signal Processing Magazine, a member of the editorial board of IEEE SPS Education Center, and the chair of the IEEE Signal Processing Society Data Science Initiative. Previously, he was chair of the IEEE Audio and Acoustic Signal Processing Technical Committee in 2017–2018. He has also held other roles, such as the general co-chair of the 2010 International Workshop on Acoustic Signal Enhancement and the 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. He is the general co-chair of Interspeech 2024, which will be held in September in Greece. His research interests include statistical signal processing and machine learning in the audio processing domain. The methods he develops utilize multi-microphone and multi-modal information. Applications include speech enhancement, noise reduction, speaker separation and diarization, dereverberation, speaker localization, and tracking. Sharon Gannot is the recipient of the 2022 European Association for Signal Processing Group Technical Achievement Award and a Fellow of the IEEE.