We consider an energy functional that arises in micromagnetic and liquid crystal theory on thin films. In particular, our energy comprises a non-convex term that models anti-symmetric exchange as well as an anisotropy term. We devise an algorithm for energy minimization in the continuous case and show weak convergence of a subsequence towards a solution of the corresponding Euler--Lagrange equation. Furthermore, an algorithm for numerical energy minimization is presented. We show empirically that this numerical algorithm converges to the correct solutions for a benchmark problem without the need for user-supplied parameters, and present a rigorous convergence analysis for important special cases.

Yellow fever virus (YFV) is a re-emerging flavivirus that causes severe hepatic disease and mortality in humans. Despite being researched for over a century, the structure of YFV has remained elusive. Here we use a chimeric virus platform to resolve the first high resolution cryo-EM structures of YFV. Stark differences in particle morphology and homogeneity are observed between vaccine and virulent strains of YFV, and these are found to have significant implications on antibody recognition and neutralisation. We identify a single residue (R380) in the YFV envelope protein that stabilises the virion surface, and leads to reduced exposure of the cross-reactive fusion loop epitope. The differences in virion morphology between YFV strains also contribute to the reduced sensitivity of the virulent YFV virions to vaccine-induced antibodies. These findings have significant implications for YFV biology, vaccinology and structure-based flavivirus antigen design.

Focused ion beam scanning electron microscopy (FIB-SEM) (1–3) has been used in life sciences to produce large volumetric datasets with high resolution information on ultrastructure of biological organisms. 3D image acquisition is accomplished by serial removal of thin layers of material using focused ion beam (FIB) milling followed by scanning electron microscopy (SEM) imaging.One of the challenges in the standard FIB-SEM imaging protocol is that FIB milling results in characteristic artifacts, known as “streaks” or “curtains”. These streaks are caused by non-uniform material removal forming long straight trenches parallel to the FIB milling direction. These artifacts get worse along the milling direction and ultimately limit size of the SEM field of view.Various methods have been proposed to mitigate the streaks in acquired images. While these techniques often provide noticeable visual improvement, the underlying problem remains. The structural information in the “streaked” areas is lost due to non-uniform material removal during milling and cannot be fully recovered.We propose a simple modification allowing for a significant reduction of milling non-uniformities of streaks. We demonstrate the effectiveness of this approach on various samples.

Epigenome is sensitive to metabolic inputs and crucial for aging. Lysosomes emerge as a signaling hub to sense metabolic cues and regulate longevity. We unveil that lysosomal metabolic pathways signal through the epigenome to regulate transgenerational longevity in Caenorhabditis elegans. We discovered that the induction of lysosomal lipid signaling and lysosomal AMP-activated protein kinase (AMPK), or the reduction of lysosomal mechanistic-target-of-rapamycin (mTOR) signaling, increases the expression of histone H3.3 variant and elevates H3K79 methylation, leading to lifespan extension across multiple generations. This transgenerational pro-longevity effect requires intestine-to-germline transportation of H3.3 and a germline-specific H3K79 methyltransferase, and can be recapitulated by overexpressing H3.3 or the H3K79 methyltransferase. This work uncovers a lysosome-epigenome signaling axis linking soma and germline to mediate the transgenerational inheritance of longevity.

bioRxiv preprint: https://www.biorxiv.org/content/early/2025/05/23/2025.05.21.652954

Animals efficiently learn to navigate their environment. In the laboratory, naive mice explore their environment via highly structured trajectories and can learn to localize new spatial targets in as few as a handful of trials. It is unclear how such efficient learning is possible, since existing computational models of spatial navigation require far more experience to achieve comparable performance and do not attempt to explain the evolving structure of animal behavior during learning. To inform a new algorithm for rapid learning of navigational goals, we took inspiration from the reliable structure of behavior as mice learned to intercept hidden spatial targets. We designed agents that generate behavioral trajectories by controlling the speed and angular velocity of smooth path segments between anchor points. To rapidly learn good anchors, we use Bayesian inference on the history of rewarded and unrewarded trajectories to infer the probability that an anchor will be successful, and active sampling to trim hypothesized anchors. Agents learn within tens of trials to generate compact trajectories that intercept a target, capturing the evolution of behavioral structure and matching the upper limits of learning efficiency observed in mice. We further show that this algorithm can explain how mice avoid obstacles and rapidly adapt to target switches. Finally, we show that this framework naturally encompasses both egocentric and allocentric strategies for navigation.

Priming is a process by which exposure to a stimulus affects the response to a subsequent stimulus in humans. In this study we found that in Drosophila, a prior encounter with an aversive stimulus results in enhanced preference for a following novel odor, while an appetitive stimulus leads to reduced preference for a new odor. This priming behavior of flies relies on the well-studied olfactory memory circuits including Kenyon cells (KC), dopaminergic neurons (DANs), and mushroom body output neurons (MBONs). Aversive stimulus results in increased odor responses in reward DANs that innervate the γ4 lobe of the mushroom body (MB) and decreased odor responses in a γ4γ5-innvervating repulsive MBON. We concluded that these neurons are required for the priming effects in flies. These results characterized the newly found priming behavior in flies and demonstrated the sheer influence of unconditioned stimulus on odor perception during associative learning.

The mammalian cerebral cortex is composed of neurons whose properties vary in a continuous fashion rather than falling into discrete cell types. In the mouse visual cortex, excitatory neurons in layer 2 and 3 (L2/3) form such a continuum along cortical depth, patterned by the graded expression of hundreds of genes. Here we sought to understand how this continuum develops and contributes to cortical wiring. Using single-nucleus multiomics (RNA- and ATAC-Seq) and spatial transcriptomics, we show that the L2/3 continuum is established in two phases. During the first postnatal week, a genetically hardwired program establishes a primitive continuum of cell identities spanning the depth of L2/3. The second program, promoted by visual experience, is later superimposed upon the preexisting continuum. This second phase is driven by activity-regulated transcription factors that drive the L2/3 depth-dependent expression of genes linked to synaptic function and plasticity. We show that neurons at different positions along the L2/3 continuum project preferentially to distinct higher visual areas and that visual deprivation disrupts targeting to some higher visual areas while sparing others. Thus, cortical continua emerge through a stepwise process in which genetic programs and sensory experience specify neuronal identity and sculpt intracortical wiring specificity.

Descending neurons (DNs) occupy a key position in the sensorimotor hierarchy, conveying signals from the brain to the rest of the body below the neck. In Drosophila melanogaster flies, approximately 480 DN cell types have been described from electron-microscopy image datasets. Genetic access to these cell types is crucial for further investigation of their role in generating behaviour. We previously conducted the first large-scale survey of Drosophila melanogaster DNs, describing 98 unique cell types from light microscopy and generating cell-type-specific split-Gal4 driver lines for 65 of them. Here, we extend our previous work, describing the morphology of 146 additional DN types from light microscopy, bringing the total number DN types identified in light microscopy datasets to 244, or roughly 50% of all DN types. In addition, we produced 500 new sparse split-Gal4 driver lines and compiled a list of previously published DN lines from the literature for a combined list of 806 split-Gal4 driver lines targeting 190 DN types.

The brain exhibits rich oscillatory dynamics that play critical roles in vigilance and cognition, such as the neural rhythms that define sleep. These rhythms continuously fluctuate, signaling major changes in vigilance, but the widespread brain dynamics underlying these oscillations are difficult to investigate. Using simultaneous EEG and fast fMRI in humans who fell asleep inside the scanner, we developed a machine learning approach to investigate which fMRI regions and networks predict fluctuations in neural rhythms. We demonstrated that the rise and fall of alpha (8-12 Hz) and delta (1-4 Hz) power-two canonical EEG bands critically involved with cognition and vigilance-can be predicted from fMRI data in subjects that were not present in the training set. This approach also identified predictive information in individual brain regions across the cortex and subcortex. Finally, we developed an approach to identify shared and unique predictive information, and found that information about alpha rhythms was highly separable in two networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale primarily across the cortex. These results demonstrate that EEG rhythms can be predicted from fMRI data, identify large-scale network patterns that underlie alpha and delta rhythms, and establish a novel framework for investigating multimodal brain dynamics.

Predictive coding is a theoretical framework that can explain how animals build internal models of their sensory environments by predicting sensory inputs. Predictive coding may capture either spatial or temporal relationships between sensory objects. While the original theory by Rao and Ballard, 1999 described spatial predictive coding, much of the recent experimental data has been interpreted as evidence for temporal predictive coding. Here we directly tested whether the “mismatch” neural responses in sensory cortex are due to a spatial or a temporal internal model. We adopted two common paradigms to study predictive coding: one based on virtual-reality and one based on static images. After training mice with repeated visual stimulation for several days, we performed multiple manipulations, including: 1) we introduced a novel stimulus, 2) we replaced a stimulus with a novel gray wall, 3) we duplicated a trained stimulus, or 4) we altered the order of the stimuli. The first two manipulations induced a substantial mismatch response in neural populations of up to 20,000 neurons recorded across primary and higher-order visual cortex, while the third and fourth ones did not. Thus, a mismatch response only occurred if a new spatial – not temporal – pattern was introduced.