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Neural substrates of parallel devaluation-sensitive and devaluation-insensitive Pavlovian learning in humans
No full textPavlovian learning depends on multiple and parallel associations leading to distinct classes of conditioned responses that vary in their flexibility following changes in the value of an associated outcome. Here, we aimed to differentiate brain areas involved in learning and encoding associations that are sensitive to changes in the value of an outcome from those that are not sensitive to such changes. To address this question, we combined a Pavlovian learning task with outcome devaluation, eye–tracking and functional magnetic resonance imaging. We used computational modeling to identify brain regions involved in learning stimulus-reward associations and stimulus–stimulus associations, by testing for brain areas correlating with reward–prediction errors and state-prediction errors, respectively. We found that, contrary to theoretical predictions about reward prediction errors being exclusively model–free, voxels correlating with reward prediction errors in the ventral striatum and subgenual anterior cingulate cortex were sensitive to devaluation. On the other hand, brain areas correlating with state prediction errors were found to be devaluation insensitive. In a supplementary analysis, we distinguished brain regions encoding predictions about outcome taste identity from those involved in encoding predictions about its expected spatial location. A subset of regions involved in taste identity predictions were devaluation sensitive while those involved in encoding predictions about spatial location were devaluation insensitive. These findings provide insights into the role of multiple associative mechanisms in the brain in mediating Pavlovian conditioned behavior – illustrating how distinct neural pathways can in parallel produce both devaluation sensitive and devaluation insensitive behaviors
Fast, accurate ranking of engineered proteins by receptor binding propensity using structural modeling
No full textDeep learning-based methods for protein structure prediction have achieved unprecedented accuracy. However, the power of these tools to guide the engineering of protein-based therapeutics remains limited due to a gap between the ability to predict the structures of candidate proteins and the ability to assess which of those proteins are most likely to bind to a target receptor. Here we bridge this gap by introducing Automated Pairwise Peptide-Receptor AnalysIs for Screening Engineered proteins (APPRAISE), a method for predicting the receptor binding propensity of engineered proteins. After generating models of engineered proteins competing for binding to a target using an established structure-prediction tool such as AlphaFold2-multimer or ESMFold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As a proof-of-concept, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent engineered adeno-associated viral vectors, as well as diverse classes of engineered proteins such as miniproteins targeting the SARS-CoV-2 spike protein, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor and PD-L1. With its high accuracy, interpretability, and generalizability, APPRAISE has the potential to expand the utility of current structural prediction and accelerate protein engineering for biomedical applications
Early life experience with natural odors modifies olfactory behavior through an associative process
No full textPast work has shown that chronic exposure of Drosophila to intense monomolecular odors in early life leads to homeostatic adaptation of olfactory neural responses and behavioral habituation to the familiar odor. Here, we found that, in contrast, persistent exposure to natural odors in early life increases behavioral attraction selectively to familiar odors. Odor experience increases the attractiveness of natural odors that are innately attractive and decreases the aversiveness of natural odors that are innately aversive. These changes in olfactory behavior are unlikely to arise from changes in the sensitivity of olfactory neurons at the first stages of olfactory processing: odor-evoked output from antennal lobe projection neurons was unchanged by chronic exposure to natural odors in terms of olfactory sensitivity, relational distances between odors, or response dynamics. We reveal a requirement for additional features of the environment beyond the odor in establishing odor experience-dependent behavioral plasticity. Passive odor exposure in a featureless environment lacking strong reinforcing cues was insufficient to elicit changes in olfactory preference; however, the same odor exposure resulted in behavioral plasticity when food was present in the environment. Together, these results indicate that behavioral plasticity elicited by persistent exposure to natural odors in early life is mediated by an associative process. In addition, they highlight the importance of using naturalistic odor stimuli for investigating olfactory function
3D genome organization around nuclear speckles drives mRNA splicing efficiency
No full textThe nucleus is highly organized such that factors involved in transcription and processing of distinct classes of RNA are organized within specific nuclear bodies. One such nuclear body is the nuclear speckle, which is defined by high concentrations of protein and non-coding RNA regulators of pre-mRNA splicing. What functional role, if any, speckles might play in the process of mRNA splicing remains unknown. Here we show that genes localized near nuclear speckles display higher spliceosome concentrations, increased spliceosome binding to their pre-mRNAs, and higher co-transcriptional splicing levels relative to genes that are located farther from nuclear speckles. We show that directed recruitment of a pre-mRNA to nuclear speckles is sufficient to drive increased mRNA splicing levels. Finally, we show that gene organization around nuclear speckles is highly dynamic with differential localization between cell types corresponding to differences in Pol II occupancy. Together, our results integrate the longstanding observations of nuclear speckles with the biochemistry of mRNA splicing and demonstrate a critical role for dynamic 3D spatial organization of genomic DNA in driving spliceosome concentrations and controlling the efficiency of mRNA splicing
Emergent N = 4 supersymmetry from N = 1
No full textWe discover a four-dimensional N = 1 supersymmetric field theory that is dual to the N = 4 super Yang-Mills theory with gauge group SU(2n+1) for each n. The dual theory is constructed through the diagonal gauging of the SU(2n+1) flavor symmetry of three copies of a strongly-coupled superconformal field theory (SCFT) of Argyres-Douglas type. We find that this theory flows in the infrared to a strongly-coupled N = 1 SCFT that lies on the same conformal manifold as N = 4 super Yang-Mills with gauge group SU(2n+1). Our construction provides a hint on why certain N=1,2 SCFTs have identical central charges (a=c)
Computational Exploration of the Nature of Li⁺-Ureide Anion Catalysis on Formation of Highly Reactive Vinyl Carbocations and Subsequent C–C Bond Forming Reactions
No full textThe mechanisms of the C–H insertion reactions of vinyl carbocations formed by heterolysis of vinyl trifluoromethanesulfonates (triflates) by catalytic lithiated 1,3-bis[3,5-bis(trifluoromethyl)phenyl]urea (Li+-ureide) have been studied with ωB97X-D density functional theory. The ionization promoted by the Li⁺-ureide forms a metastable intimate ion pair complex of Li⁺-ureide-triflate anion and vinyl cation. The relative thermodynamic stabilities of isomeric alkyl cations are impacted by ion-pairing with the Li⁺-ureide-triflate anion. We show that the C–H insertion reaction of the vinyl cation intermediate is the rate-determining step and explain the effect of the aryl substituents on the formation of the vinyl cation and its C–H insertion reactivity as well as the regioselectivity of C–H activation by the vinyl cation
Kilometer-Scale Parabolic Reflector for a Radio Telescope in a Lunar Crater
No full textWe present a structural architecture for a parabolic reflector for a conceptual radio telescope on the Moon. We study a point design with a structural diameter of 1.3 kilometer and a reflector diameter of 350 meter. The structural architecture is based on the tension truss concept: a network of tensioned cables with pre-determined length to provide shape and stability, with a compliant metallic mesh stretched between the cables for radio reflectivity. The reflector hangs from the rim of the crater, and the cables are tensioned through self-weight. This method leads to a lightweight design. We present preliminary structural analyses showing acceptable surface accuracy given various error sources. We discuss two methods for packaging the reflector. The deployment is envisioned to be carried out by robotic means
Development of a physics-informed neural network to enhance wind tunnel data for aerospace design
No full textIn recent years, physics-informed neural networks (PINNs) have emerged as a novel approach to solving PDEs in many applications, including the Navier-Stokes equations in fluid mechanics. We seek to develop a tool based on PINNs that can bridge gaps in the experimental and computational methods currently utilized in aerospace design and analysis. The feasibility of such a product is demonstrated in the context of 2D steady flows over airfoil geometries. Reconstruction of full flow fields from spatially sparse measurements of pressure and flow direction is presented, motivated by prevalent practices in subsonic wind tunnel testing campaigns. Areas of algorithm improvement, directions for future research, and broader visions for the collaboration are discussed
Development of a hybrid particle-continuum solver for studying plume expansion into rarefied flows
No full textThe direct simulation Monte Carlo (DSMC) method and unsteady Navier-Stokes (NS) are combined in a hybrid formulation with an ultimate aim to model positioning-rocket lam- inar jet expansion in the lunar atmosphere. The hybrid solver uses the Schwarz technique, a classical matching procedure of the length scales and time scales between the continuum and rarefied environments. The novelty of the current work is its ability to be applied to unsteady problems and to accommodate a large variation in Knudsen number (Kn) values. The length scale coupling from the continuum to the DSMC region is determined by a criterion based on the local gradient-length of Kn, which according to the specified criterion is larger than the continuum breakdown parameter set at the value of 0.05 at the transi- tion from continuum to rarefied conditions for a jet. To this end, one-dimensional steady shock configurations with upstream Mach numbers varying between 1.7 to 8.4 are studied. Perfect agreement is achieved with measurements, indicating that spatial coupling between the rarefied and continuum regions is performed precisely. To ensure time accuracy in the coupling, the number of DSMC time steps is determined by the ratio of the continuum (i.e., NS) time step to the DSMC time step, which is governed by the mean collision time of particles. A relatively good agreement between the measurement data and current work for unsteady shock motion indicates that the hybrid framework can model time-dependent flows accurately
High-throughput identification of crystalline natural products from crude extracts enabled by microarray technology and microED
No full textThe structural determination of natural products (NPs) can be arduous due to sample heterogeneity. This often demands itera-tive purification processes and characterization of complex molecules that may only be available in miniscule quantities. Microcrystal electron diffraction (microED) has recently shown promise as a method to solve crystal structures of NPs from nanogram quantities of analyte. However, its implementation in NP discovery remains hampered by sample throughput and purity requirements akin to traditional NP-discovery workflows. In the methods described herein, we leverage the resolving power of transmission electron microscopy (TEM) and the miniaturization capabilities of DNA microarray technology to address these challenges through the establishment of an NP screening platform, array electron diffraction (ArrayED). In this workflow, an array of HPLC fractions taken from crude extracts are deposited onto TEM grids in picoliter-sized droplets. This multiplexing of analytes on TEM grids enables 1200 or more unique samples to be simultaneously inserted into a TEM equipped with an autoloader. Selected area electron diffraction analysis of these microarrayed grids allows for rapid identification of crystalline metabolites. In this study, ArrayED enabled structural characterization of 14 natural products, including four novel crystal structures and two novel polymorphs, from 20 crude extracts. Moreover, we identify several chemical species that would not be detected by standard mass spectrometry (MS) or UV/Vis and crystal forms that would not be characterized using traditional methods