The general structure of Hybrid Inflation remains a very well-motivated mechanism for lower-scale cosmic inflation in the face of improving constraints on the tensor-to-scalar ratio. However, as originally modeled, the “waterfall” field in this mechanism gives rise to a hierarchy problem (η−problem) for the inflaton after demanding standard effective field theory (EFT) control. We modify the hybrid mechanism and incorporate a discrete “twin” symmetry, thereby yielding a viable, natural and EFT-controlled model of non-supersymmetric low-scale inflation, “Twinflation”. Analogously to Twin Higgs models, the discrete exchange-symmetry with a “twin” sector reduces quadratic sensitivity in the inflationary potential to ultra-violet physics, at the root of the hierarchy problem. The observed phase of inflation takes place on a hilltop-like potential but without fine-tuning of the initial inflaton position in field-space. We also show that all parameters of the model can take natural values, below any associated EFT-cutoff mass scales and field values, thus ensuring straightforward theoretical control. We discuss the basic phenomenological considerations and constraints, as well as possible future directions.

RNA molecules are known to fold into specific structures which often play a central role in their functions and regulation. In silico folding of RNA transcripts, especially when assisted with structure profiling (SP) data, is capable of accurately elucidating relevant structural conformations. However, such methods scale poorly to the swaths of SP data generated by transcriptome-wide experiments, which are becoming more commonplace and advancing our understanding of RNA structure and its regulation at global and local levels. This has created a need for tools capable of rapidly deriving structural assessments from SP data in a scalable manner. One such tool we previously introduced that aims to process such data is patteRNA, a statistical learning algorithm capable of rapidly mining big SP datasets for structural elements. Here, we present a reformulation of patteRNA's pattern recognition scheme that sees significantly improved precision without major compromises to computational overhead. Specifically, we developed a data-driven logistic classifier which interprets patteRNA's statistical characterizations of SP data in addition to local sequence properties as measured with a nearest neighbour thermodynamic model. Application of the classifier to human structurome data reveals a marked association between detected stem-loops and RNA binding protein (RBP) footprints. The results of our application demonstrate that upwards of 30% of RBP footprints occur within loops of stable stem-loop elements. Overall, our work arrives at a rapid and accurate method for automatically detecting families of RNA structure motifs and demonstrates the functional relevance of identifying them transcriptome-wide.

Microfluidic devices have been increasingly used for low-volume liquid handling operations. However, laboratory automation of such delicate devices has lagged behind due to the lack of world-to-chip (macro-to-micro) interfaces. In this paper, we have presented the first pipette-free robotic-microfluidic interface using a microfluidic-embedded container cap, referred to as a microfluidic cap-to-dispense (μCD), to achieve a seamless integration of liquid handling and robotic automation without any traditional pipetting steps. The μCD liquid handling platform offers a generic and modular way to connect the robotic device to standard liquid containers. It utilizes the high accuracy and high flexibility of the robotic system to recognize, capture and position; and then using microfluidic adaptive printing it can achieve high-precision on-demand volume distribution. With its modular connectivity, nanoliter processability, high adaptability, and multitask capacity, μCD shows great potential as a generic robotic-microfluidic interface for complete pipette-free liquid handling automation.