Asymmetric fluctuations and self-folding of active interfaces.

We study the structure and dynamics of the interface separating a passive fluid from a microtubule-based active fluid. Turbulent-like active flows power giant interfacial fluctuations, which exhibit pronounced asymmetry between regions of positive and negative curvature. Experiments, numerical simulations, and theoretical arguments reveal how the interface breaks up the spatial symmetry of the fundamental bend instability to generate local vortical flows that lead to asymmetric interface fluctuations.

Online Screening and Virtual Patient Education for Hereditary Cancer Risk Assessment and Testing.

Objective: To use online screening and virtual patient education tools to improve the provision of hereditary cancer risk assessment.

Methods: We conducted a prospective, single-arm study in which clinicians at five U.S.

Cell non-autonomous signaling through the conserved C. elegans glycoprotein hormone receptor FSHR-1 regulates cholinergic neurotransmission.

Modulation of neurotransmission is key for organismal responses to varying physiological contexts such as during infection, injury, or other stresses, as well as in learning and memory and for sensory adaptation. Roles for cell autonomous neuromodulatory mechanisms in these processes have been well described. The importance of cell non-autonomous pathways for inter-tissue signaling, such as gut-to-brain or glia-to-neuron, has emerged more recently, but the cellular mechanisms mediating such regulation remain comparatively unexplored.

Deciphering transcript architectural complexity in bacteria and archaea.

RNA transcripts are potential therapeutic targets, yet bacterial transcripts have uncharacterized biodiversity. We developed an algorithm for transcript prediction called tp.py using it to predict transcripts (mRNA and other RNAs) in K12 and E2348/69 strains (Bacteria:gamma-Proteobacteria), strains Scott A and RO15 (Bacteria:Firmicute), strains SG17M and NN2 strains (Bacteria:gamma-Proteobacteria), and (Archaea:Halobacteria).

Deciphering Bacterial and Archaeal Transcriptional Dark Matter and Its Architectural Complexity.

Transcripts are potential therapeutic targets, yet bacterial transcripts remain biological dark matter with uncharacterized biodiversity. We developed and applied an algorithm to predict transcripts for Escherichia coli K12 and E2348/69 strains (Bacteria:gamma-Proteobacteria) with newly generated ONT direct RNA sequencing data while predicting transcripts for Listeria monocytogenes strains Scott A and RO15 (Bacteria:Firmicute), Pseudomonas aeruginosa strains SG17M and NN2 strains (Bacteria:gamma-Proteobacteria), and Haloferax volcanii (Archaea:Halobacteria) using publicly available data. From >5 million E.