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We address the problem of how individuals can integrate efficiently their private behavior with information provided by others within a group. To this end, we consider the model of collective search introduced in [https://doi.org/10.1103/PhysRevE.102.012402], under a minimal setting with no olfactory information. Agents combine a private exploratory behavior and a social imitation consisting in aligning to their neighbors, and weigh the two contributions with a single ``trust" parameter that controls their relative influence. We find that an optimal trust parameter exists even in the absence of olfactory information, as was observed in the original model. Optimality is dictated by the need to explore the minimal region of space that contains the target. An optimal trust parameter emerges from this constraint because it it tunes imitation, which induces a collective mechanism of inertia affecting the size and path of the swarm. We predict the optimal trust parameter for cohesive groups where all agents interact with one another. We show how optimality depends on the initialization of the agents and the unknown location of the target, in close agreement with numerical simulations. Our results may be leveraged to optimize the design of swarm robotics or to understand information integration in organisms with decentralized nervous systems such as cephalopods.

A packed community of exponentially proliferating microbes will spread in size exponentially. However, due to nutrient depletion, mechanical constraints, or other limitations, exponential proliferation is not indefinite, and the spreading slows. Here, we theoretically explore a fundamental question: is it possible to infer the dominant limitation type from the spreading dynamics? Using a continuum active fluid model, we consider three limitations to cell proliferation: intrinsic growth arrest (e.g., due to sporulation), pressure from other cells, and nutrient access. We find that memoryless growth arrest still results in superlinear (accelerating) spreading, but at a reduced rate. In contrast, pressure-limited growth results in linear (constant-speed) spreading in the long-time limit. We characterize how the expansion speed depends on the maximum growth rate, the limiting pressure value, and the effective fluid friction. Interestingly, nutrient-limited growth results in a phase transition: depending on the nutrient supply and how efficiently nutrient is converted to biomass, the spreading can be either superlinear or sublinear (decelerating). We predict the phase boundary in terms of these parameters and confirm with simulations. Thus, our results suggest that when an expansion slowdown is observed, its dominant cause is likely nutrient depletion. More generally, our work suggests that cell-level growth limitations can be inferred from population-level dynamics, and it offers a methodology for connecting these two scales.

Significance: Voltage imaging microscopy has emerged as a powerful tool to investigate neural activity both in vivo and in vitro. Various imaging approaches have been developed, including point-scanning, line-scanning and wide-field microscopes, however the effects of their different temporal sampling methods on signal fidelity have not yet been fully investigated.

Aim: To provide an analysis of the inherent advantages and disadvantages of temporal sampling in scanning and wide-field microscopes and their effect on the fidelity of voltage spike detection.

Approach: We develop a mathematical framework based on a mixture of analytical modeling and computer simulations with Monte-Carlo approaches.

Results: Scanning microscopes outperform wide-field microscopes in low signal-to-noise conditions and when only a small subset of spikes needs to be detected. Wide-field microscopes outperform scanning microscopes when the measurement is temporally undersampled and a large fraction of the spikes needs to be detected. Both modalities converge in performance as sampling increases and the frame rate reaches the decay rate of the voltage indicator.

Conclusions: Our work provides guidance for the selection of optimal temporal sampling parameters for voltage imaging. Most importantly it advises against using scanning voltage imaging microscopes at frame rates below 500 Hz.

Each T cell typically carries a specific T-cell receptor (TCR) that determines its specificity against an epitope presented by the HLA complex on a target cell. Antigenic challenge triggers the expansion of reactive cells within a diverse pool of T cells with randomly generated receptors, a process that results in epitope-driven shifts of TCR frequencies over time. Here, we analyze the effects of SARS-CoV-2 vaccination on the TCR populations in peripheral blood drawn from seven COVID-naive individuals, before vaccines were widely available. To identify SARS-CoV-2 vaccine-associated TCR sequences among the ~ 10⁵ - 10⁶ TCR sequences sampled before and after vaccination, we develop statistical criteria to detect significant increases in abundance of positive TCR clones. Application of our statistical methods shows a robust identification of TCR sequences that respond to SARS-CoV-2 vaccination in vivo, illustrating the feasibility of quantifying the clone-specific dynamics of T-cell abundance changes following immunological perturbations.

Photoacoustic computed tomography (PACT) is a promising imaging modality that combines the advantages of optical contrast with ultrasound detection. Utilizing ultrasound transducers with larger surface areas can improve detection sensitivity. However, when computationally efficient analytic reconstruction methods that neglect the spatial impulse responses (SIRs) of the transducer are employed, the spatial resolution of the reconstructed images will be compromised. Although optimization-based reconstruction methods can explicitly account for SIR effects, their computational cost is generally high, particularly in three-dimensional (3D) applications. To address the need for accurate but rapid 3D PACT image reconstruction, this study presents a framework for establishing a learned SIR compensation method that operates in the data domain. The learned compensation method maps SIR-corrupted PACT measurement data to compensated data that would have been recorded by idealized point-like transducers. Subsequently, the compensated data can be used with a computationally efficient reconstruction method that neglects SIR effects. Two variants of the learned compensation model are investigated that employ a U-Net model and a specifically designed, physics-inspired model, referred to as Deconv-Net. A fast and analytical training data generation procedure is also a component of the presented framework. The framework is rigorously validated in virtual imaging studies, demonstrating resolution improvement and robustness to noise variations, object complexity, and sound speed heterogeneity. When applied to in-vivo breast imaging data, the learned compensation models revealed fine structures that had been obscured by SIR-induced artifacts. To our knowledge, this is the first demonstration of learned SIR compensation in 3D PACT imaging.

Phagocytosis is a fundamental process of the innate immune system, yet the physical determinants that govern the engulfment of soft, deformable targets remain poorly understood. Existing theoretical models typically approximate targets as rigid particles, overlooking the fact that both immune cells and many biological targets undergo significant membrane deformation during contact. Here, we develop a Monte Carlo-based membrane simulation framework to model the interactions of multiple vesicles, enabling us to explore phagocytosis-like processes in systems where both the phagocyte and the target possess flexible, thermally fluctuating membranes. We first validate our approach against established observations for the engulfment of rigid objects. We then investigate how the mechanical properties of a soft target-specifically membrane bending rigidity govern the outcome of phagocytic interactions. Our simulations reveal three distinct mechanical regimes: (i) biting or trogocytosis, in which the phagocyte extracts a portion of the target vesicle; (ii) pushing, where the target is displaced rather than engulfed; and (iii) full engulfment, in which the target is completely internalized. Increasing membrane tension via internal pressure produces analogous transitions, demonstrating a unified mechanical origin for these behaviours. Qualitative comparison with experiments involving Giant Unilamellar Vesicles (GUVs, deformable microparticles) and lymphoma cells supports the relevance of these regimes to biological phagocytosis. Together, these results highlight how target deformability fundamentally shapes phagocytic success and suggest that immune cells may exploit mechanical cues to recognize among different classes of soft targets.

High-dimensional neuroimaging data poses a challenge for the clinical assessment of neurodegenerative diseases, as it involves complex non-linear relationships that are difficult to disentangle using traditional methods. Variational Autoencoders (VAEs) provide a powerful framework for encoding neuroimaging scans into lower-dimensional latent spaces that capture meaningful disease-related features. In this work, we propose a semi-supervised VAE framework that incorporates a flexible similarity regularization term designed to align selected latent variables with clinical or biomarker measures related to dementia progression. This approach allows adapting the similarity metric and the supervised variables according to specific goals or available data. We demonstrate the framework using Positron Emission Tomography (PET) scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, guiding the model to capture neurodegenerative patterns associated with Alzheimer's Disease (AD) by maximizing the similarity between the first latent dimension and a clinical cognitive score. Leveraging the supervised latent variable, we generate average reconstructions corresponding to different levels of cognitive impairment. A voxel-wise General Linear Model (GLM) reveals reduced metabolism in key brain regions, predominantly in the hippocampus, and within major Resting State Network (RSN)s, particularly the Default Mode Network (DMN) and the Central Executive Network (CEN). Further examination of the remaining latent variables show that they encode affine transformations-rotation, translation, and scaling-as well as intensity variations, capturing common confounding factors such as inter-subject variability and site-related noise. Our findings indicate that the framework effectively extracts disease related patterns aligned with established Alzheimer's biomarkers, providing an interpretable and adaptable tool for studying neurodegenerative progression.

Nitinol (NiTi shape-memory alloy) is an interesting candidate in various medical applications like dental, orthopedic, and cardiovascular devices, owing to its unique mechanical behaviors and proper biocompatibility. The aim of this work is the local controlled delivery of a cardiovascular drug, heparin, loaded onto nitinol treated by electrochemical anodizing and chitosan coating. In this regard, the structure, wettability, drug release kinetics, and cell cytocompatibility of the specimens were analyzed in vitro. The two-stage anodizing process successfully developed a regular nanoporous layer of Ni-Ti-O on nitinol, which considerably decreased the sessile water contact angle and induced hydrophilicity. The application of the chitosan coatings controlled the release of heparin mainly by a diffusional mechanism, where the drug release mechanisms were evaluated by the Higuchi, first-order, zero-order, and Korsmeyer-Pepass models. Human umbilical cord endothelial cells (HUVECs) viability assay also showed the non-cytotoxicity of the samples, so that the best performance was found for the chitosan-coated samples. It is concluded that the designed drug delivery systems are promising for cardiovascular, particularly stent applications.

Visual illusions provide a window into the mechanisms underlying visual processing, and dynamical neural circuit models offer a natural framework for proposing and testing theories of their emergence. We propose and analyze a delay-coupled neural field model that explains stroboscopic percepts arising from the subsampling of a moving, often rotating, stimulus, such as the wagon-wheel illusion. Motivated by the role of activity propagation delays in shaping visual percepts, we study neural fields with both uniform and spatially dependent delays, representing the finite time required for signals to travel along axonal projections. Each module is organized as a ring of neurons encoding angular preference, with instantaneous local coupling and delayed long-range coupling strongest between neurons with similar preference. We show that delays generate a family of coexisting traveling bump solutions with distinct, quantized propagation speeds. Using interface-based asymptotic methods, we reduce the neural field dynamics to a low-dimensional system of coupled delay differential equations, enabling a detailed analysis of speed selection, stability, entrainment, and state transitions. Regularly pulsed inputs induce transitions between distinct speed states, including motion opposite to the forcing direction, capturing key features of visual aliasing and stroboscopic motion reversal. These results demonstrate how delayed neural interactions organize perception into discrete dynamical states and provide a mechanistic explanation for stroboscopic visual illusions.

Perfusion imaging is extensively utilized to assess hemodynamic status and tissue perfusion in various organs. Computed tomography perfusion (CTP) imaging plays a key role in the early assessment and planning of stroke treatment. While CTP provides essential perfusion parameters to identify abnormal blood flow in the brain, the use of contrast agents in CTP can lead to allergic reactions and adverse side effects, along with costing USD 4.9 billion worldwide in 2022. To address these challenges, we propose a novel deep learning framework called Multitask Automated Generation of Intermodal CT perfusion maps (MAGIC). This framework combines generative artificial intelligence and physiological information to map non-contrast computed tomography (CT) imaging to multiple contrast-free CTP imaging maps. We demonstrate enhanced image fidelity by incorporating physiological characteristics into the loss terms. Our network was trained and validated using CT image data from patients referred for stroke at UF Health and demonstrated robustness to abnormalities in brain perfusion activity. A double-blinded study was conducted involving seven experienced neuroradiologists and vascular neurologists. This study validated MAGIC's visual quality and diagnostic accuracy showing favorable performance compared to clinical perfusion imaging with intravenous contrast injection. Overall, MAGIC holds great promise in revolutionizing healthcare by offering contrast-free, cost-effective, and rapid perfusion imaging.