@article {69297,
	title = {Cell division machinery drives cell-specific gene activation during differentiation in .},
	journal = {Proc Natl Acad Sci U S A},
	volume = {121},
	year = {2024},
	month = {2024 Mar 6},
	pages = {e2400584121},
	abstract = {<p>When faced with starvation, the bacterium transforms itself into a dormant cell type called a "spore". Sporulation initiates with an asymmetric division event, which requires the relocation of the core divisome components FtsA and FtsZ, after which the sigma factor σ is exclusively activated in the smaller daughter cell. Compartment-specific activation of σ requires the SpoIIE phosphatase, which displays a biased localization on one side of the asymmetric division septum and associates with the structural protein DivIVA, but the mechanism by which this preferential localization is achieved is unclear. Here, we isolated a variant of DivIVA that indiscriminately activates σ in both daughter cells due to promiscuous localization of SpoIIE, which was corrected by overproduction of FtsA and FtsZ. We propose that the core components of the redeployed cell division machinery drive the asymmetric localization of DivIVA and SpoIIE to trigger the initiation of the sporulation program.</p>
},
	issn = {1091-6490},
	doi = {10.1073/pnas.2400584121},
	author = {Chareyre, Sylvia and Li, Xuesong and Anjuwon-Foster, Brandon R and Updegrove, Taylor B and Clifford, Sarah and Brogan, Anna P and Su, Yijun and Zhang, Lixia and Chen, Jiji and Shroff, Hari and Ramamurthi, Kumaran S}
}
@article {69558,
	title = {Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation.},
	journal = {Sci Rep},
	volume = {14},
	year = {2024},
	month = {2024 Aug 06},
	pages = {18184},
	abstract = {<p>Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and generalization. In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), that outperforms CNN based networks for image denoising. We train a general CNNT based backbone model from pairwise high-low Signal-to-Noise Ratio (SNR) image volumes, gathered from a single type of fluorescence microscope, an instant Structured Illumination Microscope. Fast adaptation to new microscopes is achieved by fine-tuning the backbone on only 5-10 image volume pairs per new experiment. Results show that the CNNT backbone and fine-tuning scheme significantly reduces training time and improves image quality, outperforming models trained using only CNNs such as 3D-RCAN and Noise2Fast. We show three examples of efficacy of this approach in wide-field, two-photon, and confocal fluorescence microscopy.</p>
},
	issn = {2045-2322},
	doi = {10.1038/s41598-024-68918-2},
	url = {https://doi.org/10.1038/s41598-024-68918-2},
	author = {Rehman, Azaan and Zhovmer, Alexander and Sato, Ryo and Mukouyama, Yoh-Suke and Chen, Jiji and Rissone, Alberto and Puertollano, Rosa and Liu, Jiamin and Vishwasrao, Harshad D and Shroff, Hari and Combs, Christian A and Xue, Hui}
}
