Recent bcoe_be_oapolicydeposits items

Examining iron‐related off‐target binding effects of 18F‐AV1451 PET in the cortex of Aβ+ individuals

Thu, 11 Jun 2026 00:00:00 +0000

The presence of neurofibrillary tangles containing hyper-phosphorylated tau is a characteristic of Alzheimer's disease (AD) pathology. The positron emission tomography (PET) radioligand sensitive to tau neurofibrillary tangles (¹⁸F-AV1451) also binds with iron. This off-target binding effect may be enhanced in older adults on the AD spectrum, particularly those with amyloid-positive biomarkers. Here, we examined group differences in ¹⁸F-AV1451 PET after controlling for iron-sensitive measures from magnetic resonance imaging (MRI) and its relationships to tissue microstructure and cognition in 40 amyloid beta positive (Aβ+) individuals, 20 amyloid beta negative (Aβ-) with MCI and 31 Aβ- control participants. After controlling for iron, increased ¹⁸F-AV1451 PET uptake was found in the temporal lobe and hippocampus of Aβ+ participants compared to Aβ- MCI and control participants. Within the Aβ+ group, significant correlations were seen between ¹⁸F-AV1451...

Locus Coeruleus Engagement Drives Network Connectivity Dynamics In Humans And Rats

Thu, 11 Jun 2026 00:00:00 +0000

Locus Coeruleus Engagement Drives Network Connectivity Dynamics In Humans And Rats

Associations between iron and mean kurtosis in iron-rich grey matter nuclei in aging.

Thu, 11 Jun 2026 00:00:00 +0000

OBJECTIVE: Elevated kurtosis values have been observed in subcortical grey matter structures of patients with neurodegenerative diseases. Here, we examined relationships between iron measures and kurtosis in iron-rich subcortical grey matter structures.Please check and confirm the affiliation 4 for the author "Xiaoping P. Hu".Affiliation 4 for Xiaoping P. Hu was incorrect since he is not associated with that department. We have removed this affiliation. Thanks! MATERIALS AND METHODS: Multi-shell diffusion and multi-echo gradient echo acquisitions were used to derive mean kurtosis and iron measures (R₂* and magnetic susceptibility), respectively, in subcortical grey matter nuclei and white matter tracts in a discovery cohort (110 healthy older and 63 younger adults) and replication cohort (72 healthy older adults).Please confirm if the author names are presented accurately and in the correct sequence (Ilana J. Bennett and Xiaoping P. Hu). Also, kindly confirm the...

The regenerative role of neural crest stem cells in physical stimuli-enhanced peripheral nerve repair

Thu, 9 Apr 2026 00:00:00 +0000

Neural crest stem cells (NCSCs), capable of differentiating into neurons and Schwann cells, are essential for peripheral nerve regeneration. This study investigates the role of endogenous NCSC-like cells in mechano-electrical stimulation (MES)-enhanced peripheral nerve repair. In a critical-sized nerve injury model, MES leads to complete nerve reconnection, accompanied by a significant increase in NCSC-like cells at the injury sites. In vitro, MES promotes the simultaneous differentiation of NCSC-like cells into neurons and Schwann cells, with elevated neuregulin 1 (NRG1) expression, a key factor in Schwann cell development. Mechanistically, MES activates BMP/Smad signaling, driving neuronal differentiation and subsequent NRG1 secretion, which in turn promotes Schwann cell maturation through the ErBB/NFAT pathway. These findings demonstrate that MES enhances peripheral nerve regeneration by activating and directing stem cell differentiation, supporting a novel therapeutic approach...

Connectome-based predictive modelling predicts frailty levels in older adults

Thu, 26 Mar 2026 00:00:00 +0000

Frailty is characterized by a persistent and progressive decline in physiological reserves, leading to increased vulnerability to stressors and a heightened risk of adverse health outcomes, both physically and mentally. Despite frailty's prevalence in older adults, there is limited research on its neural substrates, especially using task-based brain functional connectivity. In this study, we used connectome-based predictive modelling (CPM) to find a linear relationship between task-based connectomes - taken from tasks that involved similar handgrip manipulations - and a separate measure of frailty: the maximum grip strength in older adults. We observed that the task-based connectomes were able to explain individual differences in grip strength, with the Subcortical and Cerebellum network, particularly the caudate nucleus, functional connectivity being the strongest predictor. These findings demonstrate that task-based functional connectomes can serve as personalized markers that...

Fuel-driven filamentous phage nanomotors

Thu, 26 Feb 2026 00:00:00 +0000

Virus-based nanocarriers have shown great potential for noninvasive delivery of drugs, diagnostics, and imaging agents to hard-to-reach anatomical locations. Yet, they largely depend on diffusion for transport, often lacking the force to actively penetrate biological barriers, and navigation to guide therapeutic agents. In these studies, the M13 bacteriophage, a linearly shaped virus, was converted from passive nanocarrier to actively propelled, fuel-driven nanomotor. Using the distinctive low symmetry of its capsid, a single Pt nanoparticle was added to one end of the M13 virus to form a tadpole-like structure. The Pt/M13 head/tail nanomotors exhibited notably enhanced diffusion in the presence of hydrogen peroxide fuel, and significantly improved uptake by SVOK3 ovarian cancer cells in vitro. Given the successes of the M13 bacteriophage as a nanocarrier, the demonstration of this simple, but comparatively mobile M13-based nanomotor platform represents an important step...

Investigating the correlation between force output, strains, and pressure for active skeletal muscle contractions.

Sat, 21 Feb 2026 00:00:00 +0000

Measuring the forces of individual muscles in a muscle group around a joint is non-trivial, and researchers have suggested using surrogates for individual muscle forces instead. Traditionally, experimentalists have shown that the force output of the skeletal muscle tissue can be correlated to the intra-muscular pressure (IMP) generated by the muscle belly. However, IMP proves difficult to measure in vivo, due to variations from sensor placement and invasiveness of the procedure. Numerical biomechanical simulations offer a tool to analyze muscle contractions, enabling new insights into the correlations among non-invasive experimentally measurable quantities, such as strains and the force output. In this work, we investigate the correlations between the muscle force output, the principal, shear and volumetric strains experienced by the muscle, as well as the pressure developed within the muscle belly as the tissue undergoes isometric contractions with varying activation profiles...

Identifying indicators of consciousness in AI systems

Thu, 15 Jan 2026 00:00:00 +0000

Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.

Air-powered logic circuits for error detection in pneumatic systems

Thu, 28 Aug 2025 00:00:00 +0000

Air-powered logic circuits for error detection in pneumatic systems

Rapid development and optimization of paper microfluidic designs using software automation

Thu, 28 Aug 2025 00:00:00 +0000

Paper microfluidic or lateral flow devices have found many applications, especially in medical diagnostics. Their low cost and ease of use makes them particularly valuable in resource-limited and point-of-care applications. However, the process of developing new paper microfluidic devices is slowed by the need to find optimal values for their various design parameters, which determine the overall size and fluid volume requirements of the device. Often, researchers must design and test several different versions of a device to find a combination of parameters that functions as intended. To accelerate the development of new paper microfluidics, we developed a software framework that automatically designs custom paper microfluidic devices for a given application. Once the user specifies the desired device parameters, the software generates printable image files of the resulting devices, ready to output to a conventional wax ink color printer and test. As a proof-of-concept, we used...

Multi-Objective Design Automation for Microfluidic Capture Chips

Thu, 28 Aug 2025 00:00:00 +0000

Microfluidic capture chips are useful for preparing or analyzing a wide range of different chemical, biological, and medical samples. A typical microfluidic capture chip contains features that capture certain targets (i.e. molecules, particles, cells) as they flow through the chip. However, creating optimal capture chip designs is difficult because of the inherent relationship between capture efficiency and flow resistance: as more capture features are added to the chip, the capture efficiency increases, but the additional features slow the flow of fluid through the chip. This paper introduces the use of multi-objective optimization to generate capture chip designs that balance the trade-off between maximizing target capture efficiency and minimizing resistance to fluid flow. Design automation for this important class of microfluidic chips has not been attempted previously. Our approach automatically produces a Pareto front of non-dominated chip designs in a reasonable amount...

Controlling Biomedical Devices Using Pneumatic Logic

Thu, 28 Aug 2025 00:00:00 +0000

Many biomedical devices are powered and controlled by electrical components. These electronics add to the cost of a device (possibly making the device too expensive for use in resource-limited or point-of-care settings) and can also render the device unsuitable for use in some environments (for example, high-humidity areas such as incubators where condensation could cause electrical short circuits, ovens where electronic components may overheat, or explosive or flammable environments where electric sparks could cause serious accidents). In this work, we show that pneumatic logic can be used to power and control biomedical devices without the need for electricity or electric components. Originally developed for controlling microfluidic “lab-on-a-chip” devices, these circuits use microfluidic valves like transistors in air-powered logic “circuits.” We show that a modification to the basic valve design—adding additional air channels in parallel through the valve—creates a “high-flow”...

Optimization of deep learning–based denoising for arterial spin labeling: Effects of averaging and training strategies

Thu, 28 Aug 2025 00:00:00 +0000

PURPOSE: Systematic study of the effects of averaging and other relevant training strategies in deep learning (DL)-based denoising is required to optimize such processing pipelines for improving the quality of arterial spin labeling (ASL) images. METHODS: Different averaging strategies, including windowed and interleaved averaging methods, and different levels of averaging before and after convolutional neural network-based and transformer-based denoising were studied. The experiments were performed on 152 single-delay ASL scans from 152 subjects, including pulsed and pseudo-continuous ASL acquisitions. Four-fold cross-validation was implemented in all experiments. The effect of including calibration scans (M₀) was studied and compared across images of different levels of signal-to-noise ratio (SNR). The generalizability of DL denoising was examined in experiments using low-SNR ground truth in training. The results were assessed using image-quality metrics including...

Characterization of the critical lift-off of a single flat-plate microchip particle in straight rectangular microchannel flows

Thu, 14 Aug 2025 00:00:00 +0000

Characterization of the critical lift-off of a single flat-plate microchip particle in straight rectangular microchannel flows

Acoustic Side Channel Attack Against DNA Synthesis Machines: Poster Abstract

Thu, 14 Aug 2025 00:00:00 +0000

Synthetic DNA molecules play an essential role in genomics research and are a promising, high-capacity data storage medium. Currently, researchers use automated DNA synthesizers to custom-build sequences of oligonucleotides (short DNA strands) using the nucleobases: Adenine (A), Guanine (G), Cytosine (C), and Thymine (T). Research laboratories invest large amounts of capital to engineer unique oligonucleotide sequences. In our work, we demonstrate the vulnerability of commonly used DNA synthesizers to acoustic side-channel attacks, where confidentiality can be breached to steal precious DNA sequences. We introduce a novel methodology to reverse engineer the acoustic noise generated by the DNA synthesizer and extract the type and order of the nucleobases delivered to the output. To the best of our knowledge, this is the first work which highlights the possibility of physical-to-cyber attacks in DNA synthesis technologies.

Enhanced neuromorphogenesis of neural stem cells via the optimization of physical stimulus-responsive signaling pathways

Tue, 5 Aug 2025 00:00:00 +0000

BackgroundNeural stem cells hold significant promise for developing in vitro nerve models due to their capacity to differentiate into diverse neural cell types. While traditional biochemical approaches often restrict differentiation to a single phenotype, limiting the ability to study critical neuron-glia interactions, physical stimuli have been explored due to their capacity to drive multi-phenotypic differentiation of neural stem cells. However, underlying molecular mechanisms mediating the physical stimulation-induced neural stem cell differentiation, with an emphasis on electrical stimulation and mechanical stimulation, remain inadequately explored, hindering the comprehensive optimization and application of physical stimulation for enhanced neuromorphogenesis.MethodsIn this study, we explored the signaling pathways driving mechano-electrical stimulation-induced multi-phenotypic differentiation of NSCs using a piezoelectric platform with signaling inhibitors. The signaling...

Controllable Doping for Tunable and Multimodal Emission in ZnS-Based Mechanoluminescent Nanocrystals

Tue, 5 Aug 2025 00:00:00 +0000

Scaling mechanoluminescent materials to the nanoscale enhances their potential for biomedical applications due to improved sensitivity, resolution, and biocompatibility. Here, we report a versatile strategy for synthesizing wavelength-tunable mechanoluminescent ZnS nanocrystals doped with Ag⁺, Cu²⁺, or Mn²⁺. The method involves coassembly of ZnS and metal sulfide nanocrystals within silica nanoreactors, followed by high-temperature calcination to induce solid-state doping and phase transformation. The resulting ZnS:Ag⁺, ZnS:Cu²⁺, and ZnS:Mn²⁺ nanocrystals exhibit focused ultrasound-induced mechanoluminescence at 480, 500, and 585 nm, respectively. Notably, ZnS:Ag⁺ also shows photoluminescence and afterglow upon UV excitation. The luminescence intensity is highly dependent on Ag⁺ concentration, with 0.15% yielding the optimal emission. These nanocrystals were further applied to stimulate neuronal...

Beyond Stress Granules: G3BP1 and G3BP2 Redundantly Suppress SARS-CoV-2 Infection

Fri, 25 Jul 2025 00:00:00 +0000

The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed unprecedented challenges to public health and economic stability. Central to SARS-CoV-2 pathogenesis is its ability to evade the host immune response by hijacking host pathways via the interaction between viral and host proteins. We identified Ras-GTPase-activating protein SH3 domain-binding protein 1/2 (G3BP1/G3BP2) as a critical host factor that interacts with the viral nucleocapsid (N) protein, emerging from a comparative analysis of proteomic data from multiple studies. We revisited the underlying molecular mechanisms by confirming the residues required for the interaction between G3BP1/G3BP2 and SARS-CoV-2 N protein and showed that this interaction disrupts stress granule formation. Intriguingly, we observed that the ablation of both G3BP1 and G3BP2 enhanced SARS-CoV-2 replication. Our data collectively supports the notion that G3BP1 and G3BP2 play a critical role in modulating...

Reasoning Goals and Representational Decisions in Computational Cognitive Neuroscience: Lessons From the Drift Diffusion Model

Thu, 3 Jul 2025 00:00:00 +0000

Computational cognitive models are powerful tools for enhancing the quantitative and theoretical rigor of cognitive neuroscience. It is thus imperative that model users-researchers who develop models, use existing models, or integrate model-based findings into their own research-understand how these tools work and what factors need to be considered when engaging with them. To this end, we developed a philosophical toolkit that addresses core questions about computational cognitive models in the brain and behavioral sciences. Drawing on recent advances in the philosophy of modeling, we highlight the central role of model users' reasoning goals in the application and interpretation of formal models. We demonstrate the utility of this perspective by first offering a philosophical introduction to the highly popular drift diffusion model (DDM) and then providing a novel conceptual analysis of a long-standing debate about decision thresholds in the DDM. Contrary to most existing work,...

Portable dual-mode microfluidic sensor for rapid and sensitive detection of DPA on chip

Thu, 22 May 2025 00:00:00 +0000

In this work, we developed a dual-mode portable device that integrated a 3D-printed microfluidic chip for detection of dipicolinic acid (DPA) on chip. The system uses a ratiometric fluorescence nanoprobe formed by embedding carbon dots (CDs) into an Eu3⁺ metal–organic framework (Eu-MOF). Upon reaction with DPA in the microchannel, red fluorescence was enhanced and blue fluorescence suppressed, enabling sensitive ratiometric detection of DPA on chip with a detection limit (LOD) of 0.04 µM. Interestingly, the composite EuMOF/CDs/DPA also exhibits peroxidase-like activity, catalyzing the oxidation of TMB into a blue-colored product (oxTMB), which allows for colorimetric detection with an LOD of 10.14 µM. To improve usability and reduce environmental or instrumental variability, incorporating a microfluidic chip into a semi-portable device and utilizing a smartphone, making the system portable and miniaturized for easy operation. In the smartphone-assisted mode, the LODs were 0.33...

Turning brain MRI into diagnostic PET: 15O-water PET CBF synthesis from multi-contrast MRI via attention-based encoder–decoder networks

Tue, 15 Apr 2025 00:00:00 +0000

Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with radiolabeled water (¹⁵O-water) is the gold-standard for the measurement of CBF in humans, however, it is not widely available due to its prohibitive costs and the use of short-lived radiopharmaceutical tracers that require onsite cyclotron production. Magnetic resonance imaging (MRI), in contrast, is more accessible and does not involve ionizing radiation. This study presents a convolutional encoder-decoder network with attention mechanisms to predict the gold-standard ¹⁵O-water PET CBF from multi-contrast MRI scans, thus eliminating the need for radioactive tracers. The model was trained and validated using 5-fold cross-validation in a group of 126 subjects consisting of healthy controls and cerebrovascular disease patients, all of whom underwent simultaneous ¹⁵O-water...

Whole-brain causal discovery using fMRI

Fri, 11 Apr 2025 00:00:00 +0000

Despite significant research, discovering causal relationships from fMRI remains a challenge. Popular methods such as Granger causality and dynamic causal modeling fall short in handling contemporaneous effects and latent common causes. Methods from causal structure learning literature can address these limitations but often scale poorly with network size and need acyclicity. In this study, we first provide a taxonomy of existing methods and compare their accuracy and efficiency on simulated fMRI from simple topologies. This analysis demonstrates a pressing need for more accurate and scalable methods, motivating the design of Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF). CaLLTiF is a constraint-based method that uses conditional independence between contemporaneous and lagged variables to extract causal relationships. On simulated fMRI from the macaque connectome, CaLLTiF achieves significantly higher accuracy and scalability than all tested...

Evaluation of affine fiber kinematics in porcine tricuspid valve leaflets using polarized spatial frequency domain imaging and planar biaxial testing

Fri, 11 Apr 2025 00:00:00 +0000

Collagen fibers are the primary load-bearing microstructural constituent of bodily soft tissues, and, when subjected to external loading, the collagen fibers reorient, uncrimp, and elongate. Specific to the atrioventricular heart valve leaflets, the collagen fiber kinematics form the basis of many constitutive models; however, some researchers claim that modeling the affine fiber kinematics (AFK) are sufficient for accurately predicting the macroscopic tissue deformations, while others state that modeling the non-affine kinematics (i.e., fiber uncrimping together with elastic elongation) is required. Experimental verification of the AFK theory has been previously performed for the mitral valve leaflets in the left-side heart; however, this same evaluation has yet to be performed for the morphologically distinct tricuspid valve (TV) leaflets in the right-side heart. In this work, we, for the first time, evaluated the AFK theory for the TV leaflets using an integrated biaxial testing-polarized...

A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues From Digital Image Correlation Measurements.

Fri, 4 Apr 2025 00:00:00 +0000

We present a data-driven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios, without postulating a specific constitutive model form nor possessing knowledge of the material microstructure. To this end, a material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve anterior leaflet, with which we build a neural operator learning model. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material microstructure properties learned implicitly from the data and naturally embedded in the network parameters. Using various combinations of loading protocols, we compare the predictivity of this framework with finite element analysis based on three conventional constitutive models. From in-distribution tests, the predictivity...

Investigation on submicron particle separation and deflection using tilted-angle standing surface acoustic wave microfluidics