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Diel rhythms are observed across taxa and are important for maintaining synchrony between the environment and organismal physiology. A striking example of this is the diel vertical migration undertaken by zooplankton, some of which, such as the 5 mm-long copepod Pleuromamma xiphias (P. xiphias), migrate hundreds of meters daily between the surface ocean and deeper waters. Some of the molecular pathways that underlie the expressed phenotype at different stages of this migration are entrained by environmental variables (e.g., day length and food availability), while others are regulated by internal clocks. We identified a series of proteomic biomarkers that vary across ocean DVM and applied them to copepods incubated in 24 h of darkness to assess circadian control. The dark-incubated copepods shared some proteomic similarities to the ocean-caught copepods (i.e., increased abundance of carbohydrate metabolism proteins at night). Shipboard-incubated copepods demonstrated a clearer distinction between night and day proteomic profiles, and more proteins were differentially abundant than in the in situ copepods, even in the absence of the photoperiod and other environmental cues. This pattern suggests that there is a canalization of rhythmic diel physiology in P. xiphias that reflects likely circadian clock control over diverse molecular pathways. 

Tailored topological stabilizer codes in two dimensions have been shown to exhibit high-storage-threshold error rates and improved subthreshold performance under biased Pauli noise. Three-dimensional (3D) topological codes can allow for several advantages including a transversal implementation of non-Clifford logical gates, single-shot decoding strategies, and parallelized decoding in the case of fracton codes, as well as construction of fractal-lattice codes. Motivated by this, we tailor 3D topological codes for enhanced storage performance under biased Pauli noise. We present Clifford deformations of various 3D topological codes, such that they exhibit a threshold error rate of 50% under infinitely biased Pauli noise. Our examples include the 3D surface code on the cubic lattice, the 3D surface code on a checkerboard lattice that lends itself to a subsystem code with a single-shot decoder, and the 3D color code, as well as fracton models such as the X-cube model, the Sierpiński model, and the Haah code. We use the belief propagation with ordered statistics decoder (BP OSD) to study threshold error rates at finite bias. We also present a rotated layout for the 3D surface code, which uses roughly half the number of physical qubits for the same code distance under appropriate boundary conditions. Imposing coprime periodic dimensions on this rotated layout leads to logical operators of weight O(n) at infinite bias and a corresponding exp[−O(n)] subthreshold scaling of the logical failure rate, where n is the number of physical qubits in the code. Even though this scaling is unstable due to the existence of logical representations with O(1) low-rate and O(n2/3) high-rate Pauli errors, the number of such representations scales only polynomially for the Clifford-deformed code, leading to an enhanced effective distance. 

Plastic optical fibers (POF) possess the potential to occupy a distinctive role in contemporary communication systems, bridging the gap between the attributes of copper cable and glass fiber. POF stands out as a more lightweight and cost-effective alternative to glass fiber, while concurrently offering significantly enhanced communication bandwidth compared to traditional copper cables of equivalent cost or weight. However, the novelty of this technology introduces a challenge, as there is limited understanding of how POF cables may behave under specific bending conditions, particularly in the case of the latest multi-core fibers. This paper outlines a research endeavor aimed at establishing a cost-effective and reproducible testing framework for assessing the light transmission properties of plastic optical fibers during various bending conditions. The methodology involves partial automation using National Instruments LabVIEW for servo motor control, and optical power measurements were taken using the Thorlabs PM100USB. The investigation encompassed measurements across diverse bending angles and radii for five distinct types of fibers: Eska MH, Eska BH, Eska GH, a Graded Index fiber, and a Multi Core Fiber. 

This study investigates the impact of under-filled and over-filled launching configurations on the transmission properties of Plastic Optical Fiber (POF). Experimental analyses on step and graded index fibers reveal differences in spatial and frequency properties under these launching conditions. Results show that the under-filled configuration leads to narrower radial profiles and Encircled Angular Flux (EAF) compared to the over-filled configuration. Additionally, under-filled launching produces better frequency response and larger bandwidth, particularly notable in shorter fibers. Results show that the under-filled launching significantly improves transmission properties, offering potential improvements in POF applications. 

The central theme in robotic manipulation is that of the robot interacting with the world through physical contact. We tend to describe that physical contact using specific words that capture the nature of the contact and the action, such as grasp, roll, pivot, push, pull, tilt, close, open etc. We refer to these situation-specific actions as manipulation primitives. Due to the nonlinear and nonsmooth nature of physical interaction, roboticists have devoted significant efforts towards studying individual manipulation primitives. However, studying individual primitives one by one is an inherently limited process, due engineering costs, overfitting to specific tasks, and lack of robustness to unforeseen variations. These limitations motivate the main contribution of this paper: a complete and general framework to autogenerate manipulation primitives. To do so, we develop the theory and computation of contact modes as a means to classify and enumerate manipulation primitives. The contact modes form a graph, specifically a lattice. Our algorithm to autogenerate manipulation primitives (AMP) performs graph-based optimization on the contact mode lattice and solves a linear program to generate each primitive. We designed several experiments to validate our approach. We benchmarked a wide range of contact scenarios and our pipeline’s runtime was consistently in the 10 s of milliseconds. In simulation, we planned manipulation sequences using AMP. In the real-world, we showcased the robustness of our approach to real-world modeling errors. We hope that our contributions will lead to more general and robust approaches for robotic manipulation. 

Abstract Many peptide hormones form an α-helix on binding their receptors^1–4, and sensitive methods for their detection could contribute to better clinical management of disease⁵. De novo protein design can now generate binders with high affinity and specificity to structured proteins^6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion⁸to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.