Abstract Conductive hydrogels are promising materials for soft strain sensing because they combine tissue-like mechanics with electrical functionality. However, many existing systems emphasize conductivity or sensitivity at the cost of mechanical reversibility, leading to pronounced hysteresis and limited cyclic stability. Here, we report a two-step fabrication strategy that decouples mechanical and electrical optimization. A highly entangled polyacrylamide (PAAm) hydrogel is first prepared as a mechanically reversible scaffold, followed by in situ polymerization of polypyrrole (PPy) to introduce conductivity, yielding a composite hydrogel (PAAm@PPy). The material exhibits soft-tissue-level stiffness (Young’s modulus of 75 kPa), high stretchability (strain at break of 246%) and low mechanical hysteresis (5.7% at 50% strain). The as-synthesized PAAm@PPy shows an apparent bulk conductivity of 0.094 S cm⁻¹ and a linear positive piezoresistive response with a gauge factor of 2.04 over 0–50% strain, together with stable cyclic sensing (500 cycles at 30% strain). A proof-of-concept finger-bending demonstration confirms its fast response and signal stability. This work establishes a conductive hydrogel design paradigm that prioritizes low hysteresis and cyclic stability through a decoupled two-step co-synthesis of highly entangled hydrogels and conductive polymer, enabling reliable strain sensing for soft electronic applications.

Abstract Design of a medical implant needs to ensure the device can be positioned accurately and removed subsequently if needed. This necessitates specific design considerations for implantation. This review is an opinion paper considering how an operation to introduce an implant into a patient may affect design, focusing on development of implantable artificial muscles (IAMs). Compatibility with endoscopic ports or the Seldinger technique will help deliver treatment with shorter hospital stay and quicker recovery. This means an IAM device has to have configurations to suit both deployment and use. Replacement of the device or removal as an emergency means it must be easy to extract without damage to the patient’s tissues, using standard surgical equipment. The implant has to be compatible with processes for sterilizing medical devices. But design of the packaging of the implant is equally important, since transfer of the device from its sterile-packaged storage state to the operating table for deployment into a patient is a vulnerable process in which contamination can occur. Implant surgery is a high-risk healthcare intervention. Those developing IAMs should consider aspects relating to surgical approach, deployment, wear, fixation, avoidance of perioperative complications and subsequent removal.

Abstract We propose a new computational-level objective function for theoretical biology and theoretical neuroscience that combines: reinforcement learning, the study of learning with feedback via rewards; rate-distortion theory, a branch of information theory that deals with compressing signals to retain relevant information; and computational mechanics, the study of minimal sufficient statistics of prediction, also known as causal states. We highlight why this proposal is likely only an approximation, but is likely to be an interesting one, and propose a new algorithm for evaluating it to obtain the newly coined ‘reward-rate manifold’. The performance of real and artificial agents in partially observable environments can be newly benchmarked using these reward-rate manifolds. To that end, we calculate an example reward-rate manifold utilizing new equations reminiscent of the Blahut–Arimoto algorithm and propose a general algorithm for computing reward-rate manifolds. Finally, we describe experiments that can probe whether or not biological organisms are resource-rational reinforcement learners, using as an example maximin strategies, as bacteria have been shown to be approximate maximiners—doing their best in the worst-case environment, regardless of what is actually happening. This proposal explains why ‘good enough’ for organisms might actually be near-optimal, if viewed correctly.

Cerebrospinal fluid (CSF) dynamics are essential in the waste clearance of the brain. Disruptions in CSF flow are linked to various neurological conditions, highlighting the need for accurate measurement of its dynamics. Current methods typically capture high-speed CSF movements or focus on a single-frequency component, presenting challenges for comprehensive analysis. This study proposes a novel approach using displacement encoding with stimulated echoes (DENSE) MRI to assess the full spectrum of CSF motion within the brain. Through simulations, we evaluated the feasibility of disentangling distinct CSF motion components, including heartbeat- and respiration-driven flows, as well as a net velocity component due to continuous CSF turnover, and tested the performance of our method under incorrect assumptions about the underlying model of CSF motion. Results demonstrate that DENSE MRI can accurately separate these components, and reliably estimate a net velocity, even when periodic physiological motions vary over time. The method proved to be robust for including low-frequency components, incorrect assumptions on the nature of the net velocity component and missing CSF components in the model. This approach offers a comprehensive measurement technique for quantifying CSF dynamics, advancing our understanding of the relative role of various drivers of CSF dynamics in brain clearance.

Many children who have been treated for a posterior-fossa tumour experience neurocognitive problems after treatment with surgery, radiotherapy and/or chemotherapy, which significantly impact their quality of life. Knowledge about these underlying mechanisms is limited at this point. The displacement encoding with stimulated echoes (DENSE) sequence magnetic resonance imaging (MRI) at 7 T can be used to measure brain tissue pulsations, and provide information on both the blood vessels and microstructure simultaneously, which are potentially relevant parameters to assess these underlying mechanisms. A single-shot multi-slice DENSE sequence was used to obtain brain motion maps from which strain maps could be derived on a voxel-wise level. The robustness of this MRI sequence was studied using the root mean square displacement that was obtained during the registration in the analysis of the DENSE series. Although the paediatric participants exhibited noticeable head movement during the MR acquisition, good-quality strain maps were still obtained, displaying expected patterns similar to those seen in adults.

A phase-sensitive diffusion tensor magnetic resonance imaging (MRI) sequence is proposed with pulse timing optimization scheme to achieve velocity resolution of less than 20 μm s-1 and an integrated image reconstruction and velocity map generation pipeline. The application of ultra-slow flow relevant to neurofluids is enabled by the use of a recently developed, ultra-high-performance brain MRI gradient system. By simultaneously reconstructing magnitude and phase data, both metrics that characterize diffusive fluid motion and coherent velocity maps are calculated non-invasively in human subjects, time-resolved over the entire cardiac cycle. The resulting acquisition and reconstruction of velocity maps in brain parenchyma, enabled by high-performance brain imaging systems, promises to be an important approach to investigating ultra-slow neurofluid flow and glymphatic circulation.

Functional magnetic resonance imaging (fMRI) captures rich physiological and neuronal information, offering insight into neurofluid dynamics, vascular health and waste clearance. Accurate cerebral vessel segmentation could greatly facilitate fluid dynamics research in fMRI. However, existing vessel identification methods, such as magnetic resonance angiography or deep-learning-based segmentation on structural MRI, cannot reliably locate cerebral vessels in fMRI space due to misregistration from inherent fMRI distortions. To address this challenge, we developed a data-driven, automatic segmentation of cerebral vessels directly within fMRI space. This approach identified large cerebral arteries and the superior sagittal sinus (SSS) by leveraging these vessels’ distinct pulsatile signal patterns during the cardiac cycle. The method was validated in a local dataset by comparing it to ground truth cerebral artery and SSS segmentations. Using the Human Connectome Project (HCP) ageing dataset, the method’s reproducibility was tested on 422 participants aged 36–90, each with four repeated fMRI scans. The method demonstrated high reproducibility, with an intraclass correlation coefficient > 0.7 in both cerebral artery and SSS segmentation volumes. This study demonstrates that large cerebral arteries and SSS can be reproducibly and automatically segmented in fMRI datasets, facilitating reliable fluid dynamics investigation in these regions.