@article{404, abstract = {We construct martingale solutions to stochastic thin-film equations by introducing a (spatial) semidiscretization and establishing convergence. The discrete scheme allows for variants of the energy and entropy estimates in the continuous setting as long as the discrete energy does not exceed certain threshold values depending on the spatial grid size $h$. Using a stopping time argument to prolongate high-energy paths constant in time, arbitrary moments of coupled energy/entropy functionals can be controlled. Having established Hölder regularity of approximate solutions, the convergence proof is then based on compactness arguments---in particular on Jakubowski's generalization of Skorokhod's theorem---weak convergence methods, and recent tools on martingale convergence. }, author = {Fischer, Julian L and Grün, Günther}, journal = {SIAM Journal on Mathematical Analysis}, number = {1}, pages = {411 -- 455}, publisher = {Society for Industrial and Applied Mathematics }, title = {{Existence of positive solutions to stochastic thin-film equations}}, doi = {10.1137/16M1098796}, volume = {50}, year = {2018}, } @misc{9813, abstract = {File S1 contains figures that clarify the following features: (i) effect of population size on the average number/frequency of SI classes, (ii) changes in the minimal completeness deficit in time for a single class, and (iii) diversification diagrams for all studied pathways, including the summary figure for k = 8. File S2 contains the code required for a stochastic simulation of the SLF system with an example. This file also includes the output in the form of figures and tables.}, author = {Bod'ová, Katarína and Priklopil, Tadeas and Field, David and Barton, Nicholas H and Pickup, Melinda}, publisher = {Genetics Society of America}, title = {{Supplemental material for Bodova et al., 2018}}, doi = {10.25386/genetics.6148304.v1}, year = {2018}, } @article{5780, abstract = {Bioluminescence is found across the entire tree of life, conferring a spectacular set of visually oriented functions from attracting mates to scaring off predators. Half a dozen different luciferins, molecules that emit light when enzymatically oxidized, are known. However, just one biochemical pathway for luciferin biosynthesis has been described in full, which is found only in bacteria. Here, we report identification of the fungal luciferase and three other key enzymes that together form the biosynthetic cycle of the fungal luciferin from caffeic acid, a simple and widespread metabolite. Introduction of the identified genes into the genome of the yeast Pichia pastoris along with caffeic acid biosynthesis genes resulted in a strain that is autoluminescent in standard media. We analyzed evolution of the enzymes of the luciferin biosynthesis cycle and found that fungal bioluminescence emerged through a series of events that included two independent gene duplications. The retention of the duplicated enzymes of the luciferin pathway in nonluminescent fungi shows that the gene duplication was followed by functional sequence divergence of enzymes of at least one gene in the biosynthetic pathway and suggests that the evolution of fungal bioluminescence proceeded through several closely related stepping stone nonluminescent biochemical reactions with adaptive roles. The availability of a complete eukaryotic luciferin biosynthesis pathway provides several applications in biomedicine and bioengineering.}, author = {Kotlobay, Alexey A. and Sarkisyan, Karen and Mokrushina, Yuliana A. and Marcet-Houben, Marina and Serebrovskaya, Ekaterina O. and Markina, Nadezhda M. and Gonzalez Somermeyer, Louisa and Gorokhovatsky, Andrey Y. and Vvedensky, Andrey and Purtov, Konstantin V. and Petushkov, Valentin N. and Rodionova, Natalja S. and Chepurnyh, Tatiana V. and Fakhranurova, Liliia and Guglya, Elena B. and Ziganshin, Rustam and Tsarkova, Aleksandra S. and Kaskova, Zinaida M. and Shender, Victoria and Abakumov, Maxim and Abakumova, Tatiana O. and Povolotskaya, Inna S. and Eroshkin, Fedor M. and Zaraisky, Andrey G. and Mishin, Alexander S. and Dolgov, Sergey V. and Mitiouchkina, Tatiana Y. and Kopantzev, Eugene P. and Waldenmaier, Hans E. and Oliveira, Anderson G. and Oba, Yuichi and Barsova, Ekaterina and Bogdanova, Ekaterina A. and Gabaldón, Toni and Stevani, Cassius V. and Lukyanov, Sergey and Smirnov, Ivan V. and Gitelson, Josef I. and Kondrashov, Fyodor and Yampolsky, Ilia V.}, issn = {00278424}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {50}, pages = {12728--12732}, publisher = {National Academy of Sciences}, title = {{Genetically encodable bioluminescent system from fungi}}, doi = {10.1073/pnas.1803615115}, volume = {115}, year = {2018}, } @article{428, abstract = {The plant hormone gibberellic acid (GA) is a crucial regulator of growth and development. The main paradigm of GA signaling puts forward transcriptional regulation via the degradation of DELLA transcriptional repressors. GA has also been shown to regulate tropic responses by modulation of the plasma membrane incidence of PIN auxin transporters by an unclear mechanism. Here we uncovered the cellular and molecular mechanisms by which GA redirects protein trafficking and thus regulates cell surface functionality. Photoconvertible reporters revealed that GA balances the protein traffic between the vacuole degradation route and recycling back to the cell surface. Low GA levels promote vacuolar delivery and degradation of multiple cargos, including PIN proteins, whereas high GA levels promote their recycling to the plasma membrane. This GA effect requires components of the retromer complex, such as Sorting Nexin 1 (SNX1) and its interacting, microtubule (MT)-associated protein, the Cytoplasmic Linker-Associated Protein (CLASP1). Accordingly, GA regulates the subcellular distribution of SNX1 and CLASP1, and the intact MT cytoskeleton is essential for the GA effect on trafficking. This GA cellular action occurs through DELLA proteins that regulate the MT and retromer presumably via their interaction partners Prefoldins (PFDs). Our study identified a branching of the GA signaling pathway at the level of DELLA proteins, which, in parallel to regulating transcription, also target by a nontranscriptional mechanism the retromer complex acting at the intersection of the degradation and recycling trafficking routes. By this mechanism, GA can redirect receptors and transporters to the cell surface, thus coregulating multiple processes, including PIN-dependent auxin fluxes during tropic responses.}, author = {Salanenka, Yuliya and Verstraeten, Inge and Löfke, Christian and Tabata, Kaori and Naramoto, Satoshi and Glanc, Matous and Friml, Jirí}, journal = {PNAS}, number = {14}, pages = { 3716 -- 3721}, publisher = {National Academy of Sciences}, title = {{Gibberellin DELLA signaling targets the retromer complex to redirect protein trafficking to the plasma membrane}}, doi = {10.1073/pnas.1721760115}, volume = {115}, year = {2018}, } @article{62, abstract = {Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.}, author = {Shabazi, Ali and Kinnison, Jeffery and Vescovi, Rafael and Du, Ming and Hill, Robert and Jösch, Maximilian A and Takeno, Marc and Zeng, Hongkui and Da Costa, Nuno and Grutzendler, Jaime and Kasthuri, Narayanan and Scheirer, Walter}, journal = {Scientific Reports}, number = {1}, publisher = {Nature Publishing Group}, title = {{Flexible learning-free segmentation and reconstruction of neural volumes}}, doi = {10.1038/s41598-018-32628-3}, volume = {8}, year = {2018}, }