A Neural Circuit Arbitrates between Persistence and Withdrawal in Hungry Drosophila

In pursuit of food, hungry animals mobilize significant energy resources and overcome exhaustion and fear. How need and motivation control the decision to continue or change behavior is not understood. Using a single fly treadmill, we show that hungry flies persistently track a food odor and increase their effort over repeated trials in the absence of reward suggesting that need dominates negative experience. We further show that odor tracking is regulated by two mushroom body output neurons (MBONs) connecting the MB to the lateral horn. These MBONs, together with dopaminergic neurons and Dop1R2 signaling, control behavioral persistence. Conversely, an octopaminergic neuron, VPM4, which directly innervates one of the MBONs, acts as a brake on odor tracking by connecting feeding and olfaction. Together, our data suggest a function for the MB in internal state-dependent expression of behavior that can be suppressed by external inputs conveying a competing behavioral drive

Imaging Inter-Edge State Scattering Centers in the Quantum Hall Regime

We use an atomic force microscope tip as a local gate to study the scattering between edge channels in a 2D electron gas in the quantum Hall regime. The scattering is dominated by individual, microscopic scattering centers, which we directly image here for the first time. The tip voltage dependence of the scattering indicates that tunneling occurs through weak links and localized states.Comment: 4 pages, 5 figure

Dynamic deep learning based super-resolution for the shallow water equations

Correctly capturing the transition to turbulence in a barotropic instability requires fine spatial resolution. To reduce computational cost, we propose a dynamic super-resolution approach where a transient simulation on a coarse meshxD;is frequently corrected using a U-net-type neural network. For the nonlinear shallow water equations, we demonstrate that a simulation with the ICON-O ocean model with a 20 km resolution plus dynamic super-resolution trained on a 2.5 km resolution achieves discretization errorsxD;comparable to a simulation with 10 km resolution. The neural network, originally developed for image-based super-resolution in post-processing, is trained to compute the difference between solutions on both meshes and is used to correct the coarsexD;mesh solution every 12 h. We show that the ML-corrected coarse solution correctly maintains a balanced flow and captures the transition to turbulence in line with the higher resolution simulation. After a 8 d simulation, the L2-error of the corrected run isxD;similar to a simulation run on the finer mesh. While mass is conserved in the corrected runs, we observe some spurious generation of kinetic energy

Decoding the PITX2-Controlled Genetic Network in Atrial Fibrillation

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia and a major risk factor for stroke, often arises through ectopic electrical impulses derived from the pulmonary veins (PVs). Sequence variants in enhancers controlling expression of the transcription factor PITX2, which is expressed in the cardiomyocytes (CMs) of the PV and left atrium (LA), have been implicated in AF predisposition. Single nuclei multiomic profiling of RNA and analysis of chromatin accessibility combined with spectral clustering uncovered distinct PV- and LA-enriched CM cell states. Pitx2-mutant PV and LA CMs exhibited gene expression changes consistent with cardiac dysfunction through cell type-distinct, PITX2-directed, cis-regulatory grammars controlling target gene expression. The perturbed network targets in each CM were enriched in distinct human AF predisposition genes, suggesting combinatorial risk for AF genesis. Our data further reveal that PV and LA Pitx2-mutant CMs signal to endothelial and endocardial cells through BMP10 signaling with pathogenic potential. This work provides a multiomic framework for interrogating the basis of AF predisposition in the PVs of humans

Metal/semiconductor superlattices containing semimetallic ErSb nanoparticles in GaSb

We demonstrate the growth by molecular beam epitaxy of a metal/semiconductor composite consisting of epitaxial semimetallic ErSb particles in a GaSb matrix. The ErSb nucleates in an island growth mode leading to the spontaneous formation of nanometer-sized particles. These particles are found to preferentially grow along a [011] direction on a (100) GaSb surface. The particles can be overgrown with GaSb to form an epitaxial superlattice consisting of ErSb particles between GaSb spacer layers. The size of the ErSb particles increases monotonically with the deposition. The carrier concentrations in the superlattices are found to be dependent on both the size and density of the ErSb particles. Smaller particles and closer layer spacings reduce the hole concentration in the film. (C) 2004 American Institute of Physics

A New Prospero and microRNA-279 Pathway Restricts CO2 Receptor Neuron Formation

CO2 sensation represents an interesting example of nervous system and behavioral evolutionary divergence. The underlying molecular mechanisms, however, are not understood. Loss of microRNA-279 in Drosophila melanogaster leads to the formation of a CO2 sensory system partly similar to the one of mosquitoes. Here, we show that a novel allele of the pleiotropic transcription factor Prospero resembles the miR-279 phenotype. We use a combination of genetics and in vitro and in vivo analysis to demonstrate that Pros participates in the regulation of miR-279 expression, and that reexpression of miR-279 rescues the pros CO2 neuron phenotype. We identify common target molecules of miR-279 and Pros in bioinformatics analysis, and show that overexpression of the transcription factors Nerfin-1 and Escargot (Esg) is sufficient to induce formation of CO2 neurons on maxillary palps. Our results suggest that Prospero restricts CO2 neuron formation indirectly via miR-279 and directly by repressing the shared target molecules, Nerfin-1 and Esg, during olfactory system development. Given the important role of Pros in differentiation of the nervous system, we anticipate that miR-mediated signal tuning represents a powerful method for olfactory sensory system diversification during evolution

A Neural Circuit Arbitrates between Persistence and Withdrawal in Hungry Drosophila.

In pursuit of food, hungry animals mobilize significant energy resources and overcome exhaustion and fear. How need and motivation control the decision to continue or change behavior is not understood. Using a single fly treadmill, we show that hungry flies persistently track a food odor and increase their effort over repeated trials in the absence of reward suggesting that need dominates negative experience. We further show that odor tracking is regulated by two mushroom body output neurons (MBONs) connecting the MB to the lateral horn. These MBONs, together with dopaminergic neurons and Dop1R2 signaling, control behavioral persistence. Conversely, an octopaminergic neuron, VPM4, which directly innervates one of the MBONs, acts as a brake on odor tracking by connecting feeding and olfaction. Together, our data suggest a function for the MB in internal state-dependent expression of behavior that can be suppressed by external inputs conveying a competing behavioral drive

Indicators of global climate change 2022: Annual update of large-scale indicators of the state of the climate system and the human influence

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision-making needs to be informed by up-To-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5-10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth's energy imbalance, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (10.5281/zenodo.8000192, Smith et al., 2023a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that human-induced warming reached 1.14 [0.9 to 1.4]g C averaged over the 2013-2022 decade and 1.26 [1.0 to 1.6]g C in 2022. Over the 2013-2022 period, human-induced warming has been increasing at an unprecedented rate of over 0.2g C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-Time high of 54±5.3GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that increases in greenhouse gas emissions have slowed, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for human influence on climat