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What are the fundamental principles that inform representation in the primate visual brain? While objects have become an intuitive framework for studying neurons in many parts of cortex, it is possible that neurons follow a more expressive organizational principle, such as encoding generic features present across textures, places, and objects. In this study, we used multielectrode arrays to record from neurons in the early (V1/V2), middle (V4), and later [posterior inferotemporal (PIT) cortex] areas across the visual hierarchy, estimating each neuron’s local operation across natural scene via “heatmaps.” We found that, while populations of neurons with foveal receptive fields across V1/V2, V4, and PIT responded over the full scene, they focused on salient subregions within object outlines. Notably, neurons preferentially encoded animal features rather than general objects, with this trend strengthening along the visual hierarchy. These results show that the monkey ventral stream is partially organized to encode local animal features over objects, even as early as primary visual cortex. 

Synopsis The trajectory of evolution is impacted by molecular constraints and biases that are difficult to validate experimentally. Repeated evolution of similar traits across the Tree of Life serves as a natural experiment to discern common factors that drive the evolution of these traits. The architecture of genomes in one-dimensional, two-dimensional, and three-dimensional space is emerging as a potential factor that may predict repeated phenotypic evolution. For example, chromatin packaging and the 3D organization of the genome within the nucleus can impose evolutionary constraints by predisposing genomic regions for particular types of mutations, while the evolution of genome sequence can also drive reorganization of chromatin. With the explosion of new library preparation and sequencing technologies that are accessible for non-model species, we envision a great opportunity to understand how genome architecture across phylogenetically disparate species may impact repeated phenotypic evolution. We provide examples of the known and potential avenues of phenotypic convergence at each level of genome architecture and how integration of these data can provide unique insights into the constraints, trajectory, and predictability of evolution. 

Quantum machine learning algorithms promise to deliver near-term, applicable quantum computation on noisy, intermediate-scale systems. While most of these algorithms leverage quantum circuits for generic applications, a recent set of proposals, called analog quantum machine learning (AQML) algorithms, breaks away from circuit-based abstractions and favors leveraging the natural dynamics of quantum systems for computation, promising to be noise-resilient and suited for specific applications such as quantum simulation. Recent AQML studies have called for determining best ansatz selection practices and whether AQML algorithms have trap-free landscapes based on theory from quantum optimal control (QOC). We address this call by systematically studying AQML landscapes on two models: those admitting black-boxed expressivity and those tailored to simulating a specific unitary evolution. Numerically, the first kind exhibits local traps in their landscapes, while the second kind is trap-free. However, both kinds violate QOC theory’s key assumptions for guaranteeing trap-free landscapes. We propose a methodology to co-design AQML algorithms for unitary evolution simulation using the ansatz’s Magnus expansion. Our methodology guarantees the algorithm has an amenable dynamical Lie algebra with independently tunable terms. We show favorable convergence in simulating dynamics with applications to metrology and quantum chemistry. We conclude that such co-design is necessary to ensure the applicability of AQML algorithms. 

Since the terahertz frequency band (0.1–1 THz) has attracted considerable attention for the upcoming sixth-generation (6G) wireless communication systems, accurate models for multipath propagation in this frequency range need to be established. Such models advantageously use the fact that multi-path components (MPCs) occur typically in clusters, i.e., groups of MPCs that have similar delays and angles. In this paper, we first analyze the limitations of a widely used clustering algorithm, Kernel-Power-Density (KPD), in evaluating an extensive THz outdoor measurement campaign at 145–146 GHz, particularly its inability to detect small clusters. We introduce a modified version, which we term multi-level KPD (ML-KPD), iteratively applying KPD to detect whether a cluster determined in the previous round is made up of multiple clusters. We first apply the method to synthetic channels to demonstrate its efficacy and select suitable values for the adaptive hyperparameters. Then, multi-level KPD is applied to our channel measurements in line-of-sight (LOS) and non-line-of-sight (NLOS) environments to determine statistics for the number of clusters and the cluster spreads. 

Cells depend on precisely regulating barrier function within the vasculature to maintain physiological stability and facilitate essential substance transport. Endothelial cells achieve this through specialized adherens and tight junction protein complexes, which govern paracellular permeability across vascular beds. Adherens junctions, anchored by vascular endothelial (VE)-cadherin and associated catenins to the actin cytoskeleton, mediate homophilic adhesion crucial for barrier integrity. In contrast, tight junctions composed of occludin, claudin, and junctional adhesion molecule A interact with Zonula Occludens proteins, reinforcing intercellular connections essential for barrier selectivity. Endothelial cell-cell junctions exhibit dynamic conformations during development, maturation, and remodeling, regulated by local biochemical and mechanical cues. These structural adaptations play pivotal roles in disease contexts such as chronic inflammation, where junctional remodeling contributes to increased vascular permeability observed in conditions from cancer to cardiovascular diseases. Conversely, the brain microvasculature’s specialized junctional arrangements pose challenges for therapeutic drug delivery due to their unique molecular compositions and tight organization. This commentary explores the molecular mechanisms underlying endothelial cell-cell junction conformations and their implications for vascular permeability. By highlighting recent advances in quantifying junctional changes and understanding mechanotransduction pathways, we elucidate how physical forces from cellular contacts and hemodynamic flow influence junctional dynamics.