Search for: All records

Abstract The meltwater streams of the McMurdo Dry Valleys are hot spots of biological diversity in the climate-sensitive polar desert landscape. Microbial mats, largely comprised of cyanobacteria, dominate the streams which flow for a brief window of time (~10 weeks) over the austral summer. These communities, critical to nutrient and carbon cycling, display previously uncharacterized patterns of rapid destabilization and recovery upon exposure to variable and physiologically detrimental conditions. Here, we characterize changes in biodiversity, transcriptional responses and activity of microbial mats in response to hydrological disturbance over spatiotemporal gradients. While diverse metabolic strategies persist between marginal mats and main channel mats, data collected from 4 time points during the austral summer revealed a homogenization of the mat communities during the mid-season peak meltwater flow, directly influencing the biogeochemical roles of this stream ecosystem. Gene expression pattern analyses identified strong functional sensitivities of nitrogen-fixing marginal mats to changes in hydrological activities. Stress response markers detailed the environmental challenges of each microhabitat and the molecular mechanisms underpinning survival in a polar desert ecosystem at the forefront of climate change. At mid and end points in the flow cycle, mobile genetic elements were upregulated across all mat types indicating high degrees of genome evolvability and transcriptional synchronies. Additionally, we identified novel antifreeze activity in the stream microbial mats indicating the presence of ice-binding proteins (IBPs). Cumulatively, these data provide a new view of active intra-stream diversity, biotic interactions and alterations in ecosystem function over a high-flow hydrological regime. 

The recent prediction that honeycomb lattices of Co2+ (3d7) ions could host dominant Kitaev interactions provides an exciting direction for exploration of new routes to stabilizing Kitaev’s quantum spin liquid in real materials. Na3Co2SbO6 has been singled out as a potential material candidate provided that spin and orbital moments couple into a Jeff = 1/2 ground state, and that the relative strength of trigonal crystal field and spin-orbit coupling acting on Co ions can be tailored. Using x-ray linear dichroism (XLD) and x-ray magnetic circular dichroism (XMCD) experiments, alongside configuration interaction calculations, we confirm the counterintuitive positive sign of the trigonal crystal field acting on Co2+ ions and test the validity of the Jeff = 1/2 description of the electronic ground state. The results lend experimental support to recent theoretical predictions that a compression (elongation) of CoO6 octahedra along (perpendicular to) the trigonal axis would drive this cobaltate toward the Kitaev limit, assuming the Jeff = 1/2 character of the electronic ground state is preserved. 

Minimizers are k-mer sampling schemes designed to generate sketches for large sequences that preserve sufficiently long matches between sequences. Despite their widespread application, learning an effective minimizer scheme with optimal sketch size is still an open question. Most work in this direction focuses on designing schemes that work well on expectation over random sequences, which have limited applicability to many practical tools. On the other hand, several methods have been proposed to construct minimizer schemes for a specific target sequence. These methods, however, require greedy approximations to solve an intractable discrete optimization problem on the permutation space of k-mer orderings. To address this challenge, we propose: (a) a reformulation of the combinatorial solution space using a deep neural network re-parameterization; and (b) a fully differentiable approximation of the discrete objective. We demonstrate that our framework, DEEPMINIMIZER, discovers minimizer schemes that significantly outperform state-of-the-art constructions on genomic sequences. 

A defining feature of cellular growth is that protein and mRNA amounts scale with cell size so that concentrations remain approximately constant, thereby ensuring similar reaction rates and efficient biosynthesis. A key component of this biosynthetic scaling is the scaling of mRNA amounts with cell size, which occurs even among cells with the same DNA template copy number. Here, we identify RNA polymerase II as a major limiting factor increasing transcription with cell size. Other components of the transcriptional machinery are only minimally limiting and the chromatin environment is largely invariant with size. However, RNA polymerase II activity does not increase in direct proportion to cell size, inconsistent with previously proposed DNA-titration models. Instead, our data support a dynamic equilibrium model where the rate of polymerase loading is proportional to the unengaged nucleoplasmic polymerase concentration. This sublinear transcriptional increase is then balanced by a compensatory increase in mRNA stability as cells get larger. Taken together, our results show how limiting RNA polymerase II and feedback on mRNA stability work in concert to ensure the precise scaling of mRNA amounts across the physiological cell size range. 

Example-guided image synthesis has been recently attempted to synthesize an image from a semantic label map and an exemplary image. In the task, the additional exemplary image serves to provide style guidance that controls the appearance of the synthesized output. Despite the controllability advantage, the previous models are designed on datasets with specific and roughly aligned objects. In this paper, we tackle a more challenging and general task, where the exemplar is an arbitrary scene image that is semantically unaligned to the given label map. To this end, we first propose a new Masked Spatial-Channel Attention (MSCA) module which models the correspondence between two unstructured scenes via cross-attention. Next, we propose an end-to-end network for joint global and local feature alignment and synthesis. In addition, we propose a novel patch-based self-supervision scheme to enable training. Experiments on the large-scale CCOO-stuff dataset show significant improvements over existing methods. Moreover, our approach provides interpretability and can be readily extended to other tasks including style and spatial interpolation or extrapolation, as well as other content manipulation.