@article {69719, title = {Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopyAbstract}, journal = {Nature Communications}, volume = {16}, year = {2025}, month = {Jan-12-2025}, abstract = {

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained {\textquoteleft}de-aberration{\textquoteright} networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in\ C. elegans\ embryos.

}, doi = {10.1038/s41467-024-55267-x}, url = {https://www.nature.com/articles/s41467-024-55267-xhttps://www.nature.com/articles/s41467-024-55267-x.pdfhttps://www.nature.com/articles/s41467-024-55267-x.pdfhttps://www.nature.com/articles/s41467-024-55267-x}, author = {Guo, Min and Wu, Yicong and Hobson, Chad M. and Su, Yijun and Qian, Shuhao and Krueger, Eric and Christensen, Ryan and Kroeschell, Grant and Bui, Johnny and Chaw, Matthew and Zhang, Lixia and Liu, Jiamin and Hou, Xuekai and Han, Xiaofei and Lu, Zhiye and Ma, Xuefei and Zhovmer, Alexander and Combs, Christian and Moyle, Mark and Yemini, Eviatar and Liu, Huafeng and Liu, Zhiyi and Benedetto, Alexandre and La Riviere, Patrick and Col{\'o}n-Ramos, Daniel and Shroff, Hari} } @article {48299, title = {The multilayer connectome of Caenorhabditis elegans.}, journal = {PLoS Computational Biology}, volume = {12}, year = {2016}, month = {2016 Dec 16}, pages = {e1005283}, abstract = {

Connectomics has focused primarily on the mapping of synaptic links in the brain; yet it is well established that extrasynaptic volume transmission, especially via monoamines and neuropeptides, is also critical to brain function and occurs primarily outside the synaptic connectome. We have mapped the putative monoamine connections, as well as a subset of neuropeptide connections, in C. elegans based on new and published gene expression data. The monoamine and neuropeptide networks exhibit distinct topological properties, with the monoamine network displaying a highly disassortative star-like structure with a rich-club of interconnected broadcasting hubs, and the neuropeptide network showing a more recurrent, highly clustered topology. Despite the low degree of overlap between the extrasynaptic (or wireless) and synaptic (or wired) connectomes, we find highly significant multilink motifs of interaction, pinpointing locations in the network where aminergic and neuropeptide signalling modulate synaptic activity. Thus, the C. elegans connectome can be mapped as a multiplex network with synaptic, gap junction, and neuromodulator layers representing alternative modes of interaction between neurons. This provides a new topological plan for understanding how aminergic and peptidergic modulation of behaviour is achieved by specific motifs and loci of integration between hard-wired synaptic or junctional circuits and extrasynaptic signals wirelessly broadcast from a small number of modulatory neurons.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1005283}, author = {Bentley, Barry and Branicky, Robyn and Barnes, Christopher L and Chew, Yee Lian and Yemini, Eviatar and Bullmore, Edward T and V{\'e}rtes, Petra E and Schafer, William R} } @article {45854, title = {A consistent muscle activation strategy underlies crawling and swimming in Caenorhabditis elegans.}, journal = {Journal of the Royal Society Interface}, volume = {12}, year = {2015}, month = {2015 Jan 6}, pages = {20140963}, abstract = {

Although undulatory swimming is observed in many organisms, the neuromuscular basis for undulatory movement patterns is not well understood. To better understand the basis for the generation of these movement patterns, we studied muscle activity in the nematode Caenorhabditis elegans. Caenorhabditis elegans exhibits a range of locomotion patterns: in low viscosity fluids the undulation has a wavelength longer than the body and propagates rapidly, while in high viscosity fluids or on agar media the undulatory waves are shorter and slower. Theoretical treatment of observed behaviour has suggested a large change in force-posture relationships at different viscosities, but analysis of bend propagation suggests that short-range proprioceptive feedback is used to control and generate body bends. How muscles could be activated in a way consistent with both these results is unclear. We therefore combined automated worm tracking with calcium imaging to determine muscle activation strategy in a variety of external substrates. Remarkably, we observed that across locomotion patterns spanning a threefold change in wavelength, peak muscle activation occurs approximately 45{\textdegree} (1/8th of a cycle) ahead of peak midline curvature. Although the location of peak force is predicted to vary widely, the activation pattern is consistent with required force in a model incorporating putative length- and velocity-dependence of muscle strength. Furthermore, a linear combination of local curvature and velocity can match the pattern of activation. This suggests that proprioception can enable the worm to swim effectively while working within the limitations of muscle biomechanics and neural control.

}, issn = {1742-5662}, author = {Butler, Victoria J and Branicky, Robyn and Yemini, Eviatar and Liewald, Jana F and Gottschalk, Alexander and Kerr, Rex A and Chklovskii, Dmitri B and Schafer, William R} }