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Using optical tweezers to simultaneously trap, charge, and measure the charge of a microparticle in air
No full textOptical tweezers are widely used as a highly sensitive tool to measure forces on micron-scale particles. One such application is the measurement of the electric charge of a particle, which can be done with high precision in liquids, air, or vacuum. We experimentally investigate how the trapping laser itself can electrically charge such a particle, in our case a ∼1 μm SiO2 sphere in air. We model the charging mechanism as a two-photon process which reproduces the experimental data with high fidelity
Gene body methylation regulates gene expression and mediates phenotypic diversity in natural Arabidopsis populations
No full textGenetic variation is generally regarded as a prerequisite for evolution. In principle, epigenetic information inherited independently of DNA sequence can also enable evolution, but whether this occurs in natural populations is unknown. Here we show that single-nucleotide and epigenetic gene body DNA methylation (gbM) polymorphisms explain comparable amounts of expression variance in Arabidopsis thaliana populations. We genetically demonstrate that gbM regulates transcription, and we identify and genetically validate many associations between gbM polymorphism and the variation of complex traits: fitness under heat and drought, flowering time and accumulation of diverse minerals. Epigenome-wide association studies pinpoint trait-relevant genes with greater precision than genetic association analyses, probably due to reduced linkage disequilibrium between gbM variants. Finally, we identify numerous associations between gbM epialleles and diverse environmental conditions in native habitats, suggesting that gbM facilitates adaptation. Overall, our results indicate that epigenetic methylation variation fundamentally shapes phenotypic diversity in a natural population
The JWST Emission Line Survey (JELS): The sizes and merger fraction of star-forming galaxies during the Epoch of Reionization
No full textWe used observations from the JWST Emission Line Survey (JELS) to measure the half-light radii (re) of 23 Hα-emitting starforming (SF) galaxies at z = 6.1 in the PRIMER/COSMOS field. Galaxy sizes were measured in JWST near-infrared camera observations in rest-frame Hα (tracing recent star formation) with the F466N and F470N narrow-band filters from JELS, and
compared against rest-R- and V -band (tracing established stellar populations) and near-ultraviolet sizes. We find a size–stellar mass(re − M∗) relationship with a slope that is consistent with literature values at lower redshifts, though offset to lowersizes. We observe a large scatter in re at low stellar mass (M∗ < 10^8.4 Mo) which we believe is the result of bursty star formation histories (SFHs) of SF galaxies at the Epoch of Reionization (EoR). We find that the stellar and ionized gas components are similar in size at z = 6.1. The evidence of already-established stellar components in these Hα emitters (HAEs) indicates previous episodes of star formation have occurred. As such, following other JELS studies finding our HAEs are undergoing a current burst of star formation, we believe our results indicate that SF galaxies at the end of the EoR have already experienced a bursty SFH. From our re − M∗ relationship, we find re,F444W = 0.76 ± 0.46 kpc for fixed stellar mass M∗ = 10^9.25 M, which is in agreement with other observations and simulations of SF galaxies in the literature. We find a close-pair (major) merger fraction of (fmaj. merger =0.44 ± 0.22) fmerger = 0.43 ± 0.11 for galaxy separations d <~ 25 kpc, which is in agreement with other z ≈ 6 studies
A century of theories of balancing selection
No full textTraits that affect organismal fitness are often highly genetically variable. This genetic variation is vital for populations to adapt to their environments, but it is also surprising given that nature – after all – ‘selects’ the best genotypes at the expense of those that fall short. Explaining the extensive genetic variation of fitness‐related traits is thus a longstanding puzzle in evolutionary biology, with cascading implications for ecology, conservation, and human health. Balancing selection – an umbrella term for scenarios in which natural selection maintains genetic variation – is a century‐old explanation to resolve this puzzle that has gained recent momentum from genome‐scale methods for detecting it. Yet evaluating whether balancing selection can, in fact, resolve the puzzle is challenging, given the logistical constraints of distinguishing balancing selection from alternative hypotheses and the daunting collection of theoretical models that formally underpin this debate. Here, we track the development of balancing selection theory over the last century and provide an accessible review of this rich collection of models. We first outline the range of biological scenarios that can generate balancing selection. We then examine how fundamental features of genetic systems – non‐random mating between individuals, ploidy levels, genetic drift, linkage, and genetic architectures of traits – have been progressively incorporated into the theory. We end by linking these theoretical predictions to ongoing empirical efforts to understand the evolutionary processes that explain genetic variation
Datasets and code for manuscript "Electrostatics overcome acoustic collapse to assemble, adapt, and activate levitated matter"
No full textDatasets and code for publication "Electrostatics overcome acoustic collapse to assemble, adapt, and activate levitated matter
PMLR
No full textWe present Mechanistic PDE Networks -- a model for discovery of governing partial differential equations from data. Mechanistic PDE Networks represent spatiotemporal data as space-time dependent linear partial differential equations in neural network hidden representations. The represented PDEs are then solved and decoded for specific tasks. The learned PDE representations naturally express the spatiotemporal dynamics in data in neural network hidden space, enabling increased modeling power. Solving the PDE representations in a compute and memory-efficient way, however, is a significant challenge. We develop a native, GPU-capable, parallel, sparse and differentiable multigrid solver specialized for linear partial differential equations that acts as a module in Mechanistic PDE Networks. Leveraging the PDE solver we propose a discovery architecture that can discovers nonlinear PDEs in complex settings, while being robust to noise. We validate PDE discovery on a number of PDEs including reaction-diffusion and Navier-Stokes equations
Langerhans cell-targeted protein delivery enhances antigen-specific cellular immune response
No full textTargeted antigen delivery to immune cells, particularly dendritic cells, has emerged as a promising strategy to enhance therapeutic efficacy of vaccines, while minimizing adverse effects associated with conventional immunization. In this study, we use our previously described small glycomimetic molecule that is selectively recognized by the Langerhans cell (LC)-specific surface receptor Langerin and demonstrate specific delivery of protein antigens to these specialized dendritic cells. Our results show that Langerin-mediated antigen delivery significantly enhances the immune response in vivo, resulting in increased expansion and activation of antigen-specific T cells, compared to immunization with unmodified antigen. We demonstrate the feasibility of our LC-targeted platform for immune cell-specific immunization with protein antigen and underscore the potential of LCs as an access point for next-generation vaccines and immunotherapies
LNCS
No full textWe introduce and construct a new proof system called Non-interactive Arguments of Knowledge or Space (NArKoS), where a space-bounded prover can convince a verifier they know a secret, while having access to sufficient space allows one to forge indistinguishable proofs without the secret.
An application of NArKoS are space-deniable proofs, which are proofs of knowledge (say for authentication in access control) that are sound when executed by a lightweight device like a smart-card or an RFID chip that cannot have much storage, but are deniable (in the strong sense of online deniability) as the verifier, like a card reader, can efficiently forge such proofs.
We construct NArKoS in the random oracle model using an OR-proof combining a sigma protocol (for the proof of knowledge of the secret) with a new proof system called simulatable Proof of Transient Space (simPoTS). We give two different constructions of simPoTS, one based on labelling graphs with high pebbling complexity, a technique used in the construction of memory-hard functions and proofs of space, and a more practical construction based on the verifiable space-hard functions from TCC’24 where a prover must compute a root of a sparse polynomial. In both cases, the main challenge is making the proofs efficiently simulatable
A novel HMBC-CC-HMQC NMR strategy for methyl assignment using triple-13C-labeled α-ketoisovalerate integrated with UCBShift 2.0
No full textIn this study, we describe an integrated approach for methyl group assignment comprising precursor-based selective methyl group labeling, a novel pulse sequence for methyl to backbone coherence transfer and chemical shift predictions using UCBShift 2.0. The utility of this novel α-ketoacid isotopologue is shown by the adaptation of an HMBC-HMQC pulse sequence that simultaneously connects geminal methyl groups of leucine and valine residues to each other and to the protein backbone. By additional 13C,2H-labeling of residues other than valine and leucine residues of the protein, important chemical shift information about neighboring residues (following valine and leucine residues) can be achieved. Thus, different valine and leucine residues in a protein can be characterized as a specific chemical shift vector. Frequency matching with predicted chemical shifts via UCBShift 2.0 using experimental data taken from a subset of the BMRB database revealed a correct assignment performance of about 90%. With applications to proteins of 60.2 kDa and 134 kDa (4 × 33.5 kDa) in size, we demonstrate that the approach provides valuable information even for very large proteins
Coarse and bi-Lipschitz embeddability of subspaces of the Gromov–Hausdorff space into Hilbert spaces
No full textWe discuss the embeddability of subspaces of the Gromov–Hausdorff space, which consists of isometry classes of compact metric spaces endowed with the Gromov–Hausdorff distance, into Hilbert spaces. These embeddings are particularly valuable for applications to topological data analysis. We prove that its subspace consisting of metric spaces with at most n points has asymptotic dimension n(n−1)∕2. Thus, there exists a coarse embedding of that space into a Hilbert space. On the contrary, if the number of points is not bounded, then the subspace cannot be coarsely embedded into any uniformly convex Banach space and so, in particular, into any Hilbert space. Furthermore, we prove that, even if we restrict to finite metric spaces whose diameter is bounded by some constant, the subspace still cannot be bi-Lipschitz embedded into any finite-dimensional Hilbert space. We obtain both nonembeddability results by finding obstructions to coarse and bi-Lipschitz embeddings in families of isometry classes of finite subsets of the real line endowed with the Euclidean–Hausdorff distance