We present sparse graph codes appropriate for use in quantum error-correction. Quantum error-correcting codes based on sparse graphs are of interest for three reasons. First, the best codes currently known for classical channels are based on sparse graphs. Second, sparse graph codes keep the number of quantum interactions associated with the quantum error correction process small: a constant number per quantum bit, independent of the blocklength. Third, sparse graph codes often offer great flexibility with respect to blocklength and rate. We believe some of the codes we present are unsurpassed by previously published quantum error-correcting codes.

A common problem in neuroscience is to elucidate the collective neural representations of behaviorally important variables such as head direction, spatial location, upcoming movements, or mental spatial transformations. Often, these latent variables are internal constructs not directly accessible to the experimenter. Here, we propose a new probabilistic latent variable model to simultaneously identify the latent state and the way each neuron contributes to its representation in an unsupervised way. In contrast to previous models which assume Euclidean latent spaces, we embrace the fact that latent states often belong to symmetric manifolds such as spheres, tori, or rotation groups of various dimensions. We therefore propose the manifold Gaussian process latent variable model (mGPLVM), where neural responses arise from (i) a shared latent variable living on a specific manifold, and (ii) a set of non-parametric tuning curves determining how each neuron contributes to the representation. Cross-validated comparisons of models with different topologies can be used to distinguish between candidate manifolds, and variational inference enables quantification of uncertainty. We demonstrate the validity of the approach on several synthetic datasets, as well as on calcium recordings from the ellipsoid body of Drosophila melanogaster and extracellular recordings from the mouse anterodorsal thalamic nucleus. These circuits are both known to encode head direction, and mGPLVM correctly recovers the ring topology expected from neural populations representing a single angular variable.

Behavioural differences across organisms, whether healthy or pathological, are closely tied to the structure of their neural circuits. Yet, the fine-scale synaptic changes that give rise to these variations remain poorly understood, in part due to persistent challenges in detecting synapses reliably and at scale. Volume electron microscopy (EM) offers the resolution required to capture synaptic architecture, but automated detection remains difficult due to sparse annotations, morphological variability, and cross-dataset domain shifts. To address this, we make three key contributions. First, we curate a diverse EM benchmark spanning four datasets across two invertebrate species: adult and larval Drosophila melanogaster, and Megaphragma viggianii (micro-WASP). Second, we propose SimpSyn, a single-stage Residual U-Net trained to predict dual-channel spherical masks around pre- and post-synaptic sites, designed to prioritize training and inference speeds and annotation efficiency over architectural complexity. Third, we benchmark SimpSyn against Buhmann et al.'s Synful [1], a state-of-the-art multi-task model that jointly infers synaptic pairs. Despite its simplicity, SimpSyn consistently outperforms Synful in F1-score across all volumes for synaptic site detection. While generalization across datasets remains limited, SimpSyn achieves competitive performance when trained on the combined cohort. Finally, ablations reveal that simple post-processing strategies - such as local peak detection and distance-based filtering - yield strong performance without complex test-time heuristics. Taken together, our results suggest that lightweight models, when aligned with task structure, offer a practical and scalable solution for synapse detection in large-scale connectomic pipelines.

Multi-band images of galaxies reveal a huge amount of information about their morphology and structure. However, inferring properties of the underlying stellar populations such as age, metallicity or kinematics from those images is notoriously difficult. Traditionally such information is best extracted from expensive spectroscopic observations. Here we present the $Painting\, IntrinsiC\, Attributes\, onto\, SDSS\, Objects$ (PICASSSO) project and test the information content of photometric multi-band images of galaxies. We train a convolutional neural network on 27,558 galaxy image pairs to establish a connection between broad-band images and the underlying physical stellar and gaseous galaxy property maps. We test our machine learning (ML) algorithm with SDSS $ugriz$ mock images for which uncertainties and systematics are exactly known. We show that multi-band galaxy images contain enough information to reconstruct 2d maps of stellar mass, metallicity, age and gas mass, metallicity as well as star formation rate. We recover the true stellar properties on a pixel by pixel basis with only little scatter, $\lesssim20\%$ compared to $\sim50\%$ statistical uncertainty from traditional mass-to-light-ratio based methods. We further test for any systematics of our algorithm with image resolution, training sample size or wavelength coverage. We find that galaxy morphology alone constrains stellar properties to better than $\sim20\%$ thus highlighting the benefits of including morphology into the parameter estimation. The machine learning approach can predict maps of high resolution, only limited by the resolution of the input bands, thus achieving higher resolution than IFU observations. The network architecture and all code is publicly available on GitHub.

We have designed, fabricated and tested a lens with chromatic aberration coefficient (Cc) of 1.0 mm, a 4.0 mm pole-gap and 2.0 mm bore that is wide enough to accommodate an anti-contamination system and an objective aperture. This lens extends the temporal-coherence envelope of the electron microscope beyond 2 Angstrom, using a low-cost Schottky FEG. We hope that this lens design can be used to improve all 100 keV electron microscopes designed for single-particle electron cryomicroscopy (cryoEM).

Mask annotation remains a significant bottleneck in AI-driven biomedical image analysis due to its labor-intensive nature. To address this challenge, we introduce SAMJ, a user-friendly ImageJ/Fiji plugin leveraging the Segment Anything Model (SAM). SAMJ enables seamless, interactive annotations with one-click installation on standard computers. Designed for real-time object delineation in large scientific images, SAMJ is an easy-to-use solution that simplifies and accelerates the creation of labeled image datasets.

Hydrogen cyanide (HCN) is a key feedstock molecule for the production of life's building blocks. The formation of HCN in an N$_2$-rich atmospheres requires first that the triple bond between N$\equiv$N be severed, and then that the atomic nitrogen find a carbon atom. These two tasks can be accomplished via photochemistry, lightning, impacts, or volcanism. The key requirements for producing appreciable amounts of HCN are the free availability of N$_2$ and a local carbon to oxygen ratio of C/O $\geq 1$. We discuss the chemical mechanisms by which HCN can be formed and destroyed on rocky exoplanets with Earth-like N$_2$ content and surface water inventories, varying the oxidation state of the dominant carbon-containing atmospheric species. HCN is most readily produced in an atmosphere rich in methane (CH$_4$) or acetylene (C$_2$H$_2$), but can also be produced in significant amounts ($> 1$ ppm) within CO-dominated atmospheres. Methane is not necessary for the production of HCN. We show how destruction of HCN in a CO$_2$-rich atmosphere depends critically on the poorly-constrained energetic barrier for the reaction of HCN with atomic oxygen. We discuss the implications of our results for detecting photochemically produced HCN, for concentrating HCN on the planet's surface, and its importance for prebiotic chemistry.

Ewald sphere curvature correction, which extends beyond the projection approximation, stretches the shallow depth of field in cryo-EM reconstructions of thick particles. Here we show that even for previously assumed thin particles, reconstruction artifacts which we refer to as ghosts can appear. By retrieving the lost phases of the electron exitwaves and accounting for the first Born approximation scattering within the particle, we show that these ghosts can be effectively eliminated. Our simulations demonstrate how such ghostbusting can improve reconstructions as compared to existing state-of-the-art software. Like ptychographic cryo-EM, our Ghostbuster algorithm uses phase retrieval to improve reconstructions, but unlike the former, we do not need to modify the existing data acquisition pipelines.

We develop Self2Seg, a self-supervised method for the joint segmentation and denoising of a single image. To this end, we combine the advantages of variational segmentation with self-supervised deep learning. One major benefit of our method lies in the fact, that in contrast to data-driven methods, where huge amounts of labeled samples are necessary, Self2Seg segments an image into meaningful regions without any training database. Moreover, we demonstrate that self-supervised denoising itself is significantly improved through the region-specific learning of Self2Seg. Therefore, we introduce a novel self-supervised energy functional in which denoising and segmentation are coupled in a way that both tasks benefit from each other. We propose a unified optimisation strategy and numerically show that for noisy microscopy images our proposed joint approach outperforms its sequential counterpart as well as alternative methods focused purely on denoising or segmentation.

As the characterization of exoplanet atmospheres proceeds, providing insights into atmospheric chemistry and composition, a key question is how much deeper into the planet we might be able to see from its atmospheric properties alone. For small planets with modest atmospheres and equilibrium temperatures, the first layer below the atmosphere will be their rocky surface. For such warm rocky planets, broadly Venus-like planets, the high temperatures and moderate pressures at the base of their atmospheres may enable thermochemical equilibrium between rock and gas. This links the composition of the surface to that of the observable atmosphere. Using an equilibrium chemistry code, we find a boundary in surface pressure-temperature space which simultaneously separates distinct mineralogical regimes and atmospheric regimes, potentially enabling inference of surface mineralogy from spectroscopic observations of the atmosphere. Weak constraints on the surface pressure and temperature also emerge. This regime boundary corresponds to conditions under which SO2 is oxidized and absorbed by calcium-bearing minerals in the crust, thus the two regimes reflect the sulphidation of the crust. The existence of these atmospheric regimes for Venus-like planets is robust to plausible changes in the elemental composition. Our results pave the way to the prospect of characterizing exoplanetary surfaces as new data for short period rocky planet atmospheres emerge.

Long-standing unexplained Venus atmosphere observations and chemical anomalies point to unknown chemistry but also leave room for the possibility of life. The unexplained observations include several gases out of thermodynamic equilibrium (e.g. tens of ppm O2, the possible presence of PH3 and NH3, SO2 and H2O vertical abundance profiles), an unknown composition of large, lower cloud particles, and the "unknown absorber(s)". Here we first review relevant properties of the Venus atmosphere and then describe the atmospheric chemical anomalies and how they motivate future astrobiology missions to Venus.

Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. Along with knowledge about the imaged molecules, cryo-EM maps allow de novo atomic modelling, which is typically done through a laborious manual process. Taking inspiration from recent advances in machine learning applications to protein structure prediction, we propose a graph neural network (GNN) approach for automated model building of proteins in cryo-EM maps. The GNN acts on a graph with nodes assigned to individual amino acids and edges representing the protein chain. Combining information from the voxel-based cryo-EM data, the amino acid sequence data and prior knowledge about protein geometries, the GNN refines the geometry of the protein chain and classifies the amino acids for each of its nodes. Application to 28 test cases shows that our approach outperforms the state-of-the-art and approximates manual building for cryo-EM maps with resolutions better than 3.5 \r{A}.

How genes affect tissue scale organization remains a longstanding biological puzzle. As experimental efforts aim to quantify gene expression, chromatin organization, cellular structure, and tissue structure, computational modeling lags behind. To address this gap, we merge a cellular-based tissue model with a nuclear model that includes a deformable lamina shell and chromatin to test multiscale hypotheses linking chromatin and tissue scales. We propose a multiscale hypothesis focusing on brain organoids to explain structural differences between brain organoids built from induced-pluripotent human stem cells and induced-pluripotent gorilla and chimpanzee cells. Recent experiments discover that a cell fate transition from neuroepithelial to radial glial cells includes a new intermediate state delayed in human organoids, which narrows and lengthens cells on the apical side. Experiments show that the transcription factor ZEB2 plays a major role in the emergence of this intermediate state with ZEB2 mRNA levels peaking. We postulate that the enhancement of ZEB2 expression is potentially due to chromatin reorganization in response to mechanical deformations of the nucleus. A larger critical mechanical strain triggers reorganization in human-derived stem cells, causing delayed ZEB2 upregulation compared with genetically close relatives. We test this by exploring how slightly different initial configurations of chromatin reorganize under applied strain, with greater representing less genetically-close relatives. We find that larger configuration discrepancies produce increased differences in the magnitude of chromatin displacement that rise faster than linearly yet slower than exponentially. Changes in chromatin strain and contact maps can reveal species-specific differences, aiding our understanding of how one species differs in structure from another.

We demonstrate fluorescence imaging by two-photon excitation without scanning in biological specimens as previously described by Hwang and co-workers, but with an increased field size and with framing rates of up to 100 Hz. During recordings of synaptically-driven Ca$^{2+}$ events in primary rat neurone cultures loaded with the fluorescent Ca$^{2+}$ indicator Fluo-4 AM, we have observed greatly reduced photo-bleaching in comparison with single-photon excitation. This method, which requires no costly additions to the microscope, promises to be useful for work where high time-resolution is required.

The atmospheric temperatures of the ultra-hot Jupiter KELT-9b straddle the transition between gas giants and stars, and therefore between two traditionally distinct regimes of atmospheric chemistry. Previous theoretical studies assume the atmosphere of KELT-9b to be in chemical equilibrium. Despite the high ultraviolet flux from KELT-9, we show using photochemical kinetics calculations that the observable atmosphere of KELT-9b is predicted to be close to chemical equilibrium, which greatly simplifies any theoretical interpretation of its spectra. It also makes the atmosphere of KELT-9b, which is expected to be cloudfree, a tightly constrained chemical system that lends itself to a clean set of theoretical predictions. Due to the lower pressures probed in transmission (compared to emission) spectroscopy, we predict the abundance of water to vary by several orders of magnitude across the atmospheric limb depending on temperature, which makes water a sensitive thermometer. Carbon monoxide is predicted to be the dominant molecule under a wide range of scenarios, rendering it a robust diagnostic of the metallicity when analyzed in tandem with water. All of the other usual suspects (acetylene, ammonia, carbon dioxide, hydrogen cyanide, methane) are predicted to be subdominant at solar metallicity, while atomic oxygen, iron and magnesium are predicted to have relative abundances as high as 1 part in 10,000. Neutral atomic iron is predicted to be seen through a forest of optical and near-infrared lines, which makes KELT-9b suitable for high-resolution ground-based spectroscopy with HARPS-N or CARMENES. We summarize future observational prospects of characterizing the atmosphere of KELT-9b.

Suppose that we are given a quantum computer programmed ready to perform a computation if it is switched on. Counterfactual computation is a process by which the result of the computation may be learnt without actually running the computer. Such processes are possible within quantum physics and to achieve this effect, a computer embodying the possibility of running the computation must be available, even though the computation is, in fact, not run. We study the possibilities and limitations of general protocols for the counterfactual computation of decision problems (where the result r is either 0 or 1). If p(r) denotes the probability of learning the result r ``for free'' in a protocol then one might hope to design a protocol which simultaneously has large p(0) and p(1). However we prove that p(0)+p(1) never exceeds 1 in any protocol and we derive further constraints on p(0) and p(1) in terms of N, the number of times that the computer is not run. In particular we show that any protocol with p(0)+p(1)=1-epsilon must have N tending to infinity as epsilon tends to 0. These general results are illustrated with some explicit protocols for counterfactual computation. We show that "interaction-free" measurements can be regarded as counterfactual computations, and our results then imply that N must be large if the probability of interaction is to be close to zero. Finally, we consider some ways in which our formulation of counterfactual computation can be generalised.

Enzymes are biological catalysts that can accelerate chemical reactions compared to uncatalyzed reactions in aqueous environments. Their catalytic efficiency is quantified by the turnover number (kcat), a parameter in enzyme kinetics. Enhancing enzyme activity is important for optimizing slow chemical reactions, with far-reaching implications for both research and industrial applications. However, traditional wet-lab methods for measuring and optimizing enzyme activity are often resource-intensive and time-consuming. To address these limitations, we introduce kcatDiffuser, a novel regressor-guided diffusion model designed to predict and improve enzyme turnover numbers. Our approach innovatively reformulates enzyme mutation prediction as a protein inverse folding task, thereby establishing a direct link between structural prediction and functional optimization. kcatDiffuser is a graph diffusion model guided by a regressor, enabling the prediction of amino acid mutations at multiple random positions simultaneously. Evaluations on BERENDA dataset shows that kcatDiffuser can achieve a {\Delta} log kcat of 0.209, outperforming state-of-the-art methods like ProteinMPNN, PiFold, GraDe-IF in improving enzyme turnover numbers. Additionally, kcatDiffuser maintains high structural fidelity with a recovery rate of 0.716, pLDDT score of 92.515, RMSD of 3.764, and TM-score of 0.934, demonstrating its ability to generate enzyme variants with enhanced activity while preserving essential structural properties. Overall, kcatDiffuser represents a more efficient and targeted approach to enhancing enzyme activity. The code is available at this https URL.

The depletion of SO$_2$ and H$_2$O in and above the clouds of Venus (45 -- 65 km) cannot be explained by known gas-phase chemistry and the observed composition of the atmosphere. We apply a full-atmosphere model of Venus to investigate three potential explanations for the SO$_2$ and H$_2$O depletion: (1) varying the below-cloud water vapor (H$_2$O), (2) varying the below-cloud sulfur dioxide (SO$_2$), and (3) the incorporation of chemical reactions inside the sulfuric acid cloud droplets. We find that increasing the below-cloud H$_2$O to explain the SO$_2$ depletion results in a cloud top that is 20 km too high, above-cloud O$_2$ three orders of magnitude greater than observational upper limits and no SO above 80 km. The SO$_2$ depletion can be explained by decreasing the below-cloud SO$_2$ to $20\,{\rm ppm}$. The depletion of SO$_2$ in the clouds can also be explained by the SO$_2$ dissolving into the clouds, if the droplets contain hydroxide salts. These salts buffer the cloud pH. The amount of salts sufficient to explain the SO$_2$ depletion entail a droplet pH of $\sim 1$ at 50 km. Since sulfuric acid is constantly condensing out into the cloud droplets, there must be a continuous and pervasive flux of salts of $\approx 10^{-13} \, {\rm mol \, cm^{-2} \, s^{-1}}$ driving the cloud droplet chemistry. An atmospheric probe can test both of these explanations by measuring the pH of the cloud droplets and the concentrations of gas-phase SO$_2$ below the clouds.