Confined samples of liquid crystals are characterized by a variety of topological defects and can be exposed to external constraints such as extreme confinements with nontrivial topology. Here we explore the intrinsic structure of smectic colloidal layers dictated by the interplay between entropy and an imposed external topology. Considering an annular confinement as a basic example, a plethora of competing states is found with nontrivial defect structures ranging from laminar states to multiple smectic domains and arrays of edge dislocations, which we refer to as Shubnikov states in formal analogy to the characteristic of type-II superconductors. Our particle-resolved results, gained by a combination of real-space microscopy of thermal colloidal rods and fundamental-measure-based density functional theory of hard anisotropic bodies, agree on a quantitative level.Nsp15, a uridine specific endoribonuclease conserved across coronaviruses, processes viral RNA to evade detection by host defense systems. Crystal structures of Nsp15 from different coronaviruses have shown a common hexameric assembly, yet how the enzyme recognizes and processes RNA remains poorly understood. Here we report a series of cryo-EM reconstructions of SARS-CoV-2 Nsp15, in both apo and UTP-bound states. The cryo-EM reconstructions, combined with biochemistry, mass spectrometry, and molecular dynamics, expose molecular details of how critical active site residues recognize uridine and facilitate catalysis of the phosphodiester bond. Mass spectrometry revealed the accumulation of cyclic phosphate cleavage products, while analysis of the apo and UTP-bound datasets revealed conformational dynamics not observed by crystal structures that are likely important to facilitate substrate recognition and regulate nuclease activity. Collectively, these findings advance understanding of how Nsp15 processes viral RNA and provide a structural framework for the development of new therapeutics.Many transmembrane receptors have a desensitized state, in which they are unable to respond to external stimuli. The family of microbial rhodopsin proteins includes one such group of receptors, whose inactive or dark-adapted (DA) state is established in the prolonged absence of light. Here, we present high-resolution crystal structures of the ground (light-adapted) and DA states of Archaerhodopsin-3 (AR3), solved to 1.1 Å and 1.3 Å resolution respectively.  https://www.selleckchem.com/products/4-hydroxytamoxifen-4-ht-afimoxifene.html  We observe significant differences between the two states in the dynamics of water molecules that are coupled via H-bonds to the retinal Schiff Base. Supporting QM/MM calculations reveal how the DA state permits a thermodynamic equilibrium between retinal isomers to be established, and how this same change is prevented in the ground state in the absence of light. We suggest that the different arrangement of internal water networks in AR3 is responsible for the faster photocycle kinetics compared to homologs.Soil absorbs about 20% of anthropogenic carbon emissions annually, and clay is one of the key carbon-capture materials. Although sorption to clay is widely assumed to strongly retard the microbial decomposition of soil organic matter, enhanced degradation of clay-associated organic carbon has been observed under certain conditions. The conditions in which clay influences microbial decomposition remain uncertain because the mechanisms of clay-organic carbon interactions are not fully understood. Here we reveal the spatiotemporal dynamics of carbon sorption and release within model clay aggregates and the role of enzymatic decomposition by directly imaging a transparent smectite clay on a microfluidic chip. We demonstrate that clay-carbon protection is due to the quasi-irreversible sorption of high molecular-weight sugars within clay aggregates and the exclusion of bacteria from these aggregates. We show that this physically-protected carbon can be enzymatically broken down into fragments that are released into solution. Further, we suggest improvements relevant to soil carbon models.Genome sequences have been determined for many model organisms; however, repetitive regions such as centromeres, telomeres, and subtelomeres have not yet been sequenced completely. Here, we report the complete sequences of subtelomeric homologous (SH) regions of the fission yeast Schizosaccharomyces pombe. We overcame technical difficulties to obtain subtelomeric repetitive sequences by constructing strains that possess single SH regions of a standard laboratory strain. In addition, some natural isolates of S. pombe were analyzed using previous sequencing data. Whole sequences of SH regions revealed that each SH region consists of two distinct parts with mosaics of multiple common segments or blocks showing high variation among subtelomeres and strains. Subtelomere regions show relatively high frequency of nucleotide variations among strains compared with the other chromosomal regions. Furthermore, we identified subtelomeric RecQ-type helicase genes, tlh3 and tlh4, which add to the already known tlh1 and tlh2, and found that the tlh1-4 genes show high sequence variation with missense mutations, insertions, and deletions but no severe effects on their RNA expression. Our results indicate that SH sequences are highly polymorphic and hot spots for genome variation. These features of subtelomeres may have contributed to genome diversity and, conversely, various diseases.The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.