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Parkinson’s disease (PD) is one of the rapidly growing neurodegenerative diseases, affecting more than 10 million people worldwide. Early and accurate diagnosis of PD is highly desirable for therapeutic interventions but remains a substantial challenge. We developed a soft, portable intelligent keyboard leveraging magnetoelasticity to detect subtle pressure variations in keystroke dynamics by converting continuous keystrokes into high-fidelity electrical signals, thus enabling the quantitative analysis of PD motor symptoms using machine learning. Relying on a fundamental working mechanism, the intelligent keyboard demonstrates highly sensitive, intrinsically waterproof, and biocompatible properties, with the successful demonstration in a pilot study on patients with PD. To facilitate the potential continuous monitoring of PD, a customized cellphone application was developed to integrate the intelligent keyboard into a wireless platform. Together, the intelligent keyboard system’s compelling properties position it as a promising tool for advancing early diagnosis and facilitating personalized, predictive, preventative, and participatory approaches to PD healthcare. 

Utilizing heterogeneous mobile sensors to actively gather information improves adaptability and reliability in extended environments. This article presents a cooperative multirobot multitarget search and tracking framework aimed at enhancing the efficiency of the heterogeneous sensor network, and consequently, improving the overall target tracking accuracy. The concept of normalized unused sensing capacity is introduced to quantify the information a sensor is currently gathering relative to its theoretical maximum. This measurement can be computed using entirely local information and is applicable to various sensor models, distinguishing it from previous literature on the subject. It is then utilized to develop a heuristics distributed coverage control strategy for a heterogeneous sensor network, adaptively balancing the workload based on each sensor's current unused capacity. The algorithm is validated through a series of robot operating system (ROS) and MATLAB simulations, demonstrating superior results compared to standard approaches that do not account for heterogeneity or current usage rates. 

Having been predominantly observed in rigid metal and metal alloys since 1865, the magnetoelastic effect was recently experimentally discovered in a soft matter system and used as a new working mechanism for energy and health care applications. Here, a theoretical framework is presented and proven to be universally accurate and robust in interpreting the giant magnetoelastic effect across soft systems subjected to various deformation modes, micromagnet concentrations, magnetization profiles, and geometric structures. The theory uncovers substantial, unique magnetoelastic phenomena in soft systems, including the magnetic pole reversal under localized compression. This work lays a firm foundation for an in-depth understanding and practical applications of the giant magnetoelastic effect in soft matter systems. 

Abstract The severe mismatch between solid bioelectronics and dynamic biological tissues has posed enduring challenges in the biomonitoring community. Here, we developed a reconfigurable liquid cardiac sensor capable of adapting to dynamic biological tissues, facilitating ambulatory cardiac monitoring unhindered by motion artifacts or interference from other biological activities. We employed an ultrahigh-resolution 3D scanning technique to capture tomographic images of the skin on the wrist. Then, we established a theoretical model to gain a deep understanding of the intricate interaction between our reconfigurable sensor and dynamic biological tissues. To properly elucidate the advantages of this sensor, we conducted cardiac monitoring alongside benchmarks such as the electrocardiogram. The liquid cardiac sensor was demonstrated to produce stable signals of high quality (23.1 dB) in ambulatory settings. 

Hemibiotrophic fungi in the genus Colletotrichum employ a biotrophic phase to invade host epidermal cells followed by a necrotrophic phase to spread through neighboring mesophyll and epidermal cells. We used serial block face-scanning electron microscopy (SBF-SEM) to compare subcellular changes that occur in Medicago sativa (alfalfa) cotyledons during infection by Colletotrichum destructivum (compatible on M. sativa) and C. higginsianum (incompatible on M. sativa). Three-dimensional reconstruction of serial images revealed that alfalfa epidermal cells infected with C. destructivum undergo massive cytological changes during the first 60 h following inoculation to accommodate extensive intracellular hyphal growth. Conversely, inoculation with the incompatible species C. higginsianum resulted in no successful penetration events and frequent formation of papilla-like structures and cytoplasmic aggregates beneath attempted fungal penetration sites. Further analysis of the incompatible interaction using focused ion beam-scanning electron microscopy (FIB-SEM) revealed the formation of large multivesicular body-like structures that appeared spherical and were not visible in compatible interactions. These structures often fused with the host plasma membrane, giving rise to paramural bodies that appeared to be releasing extracellular vesicles (EVs). Isolation of EVs from the apoplastic space of alfalfa leaves at 60 h postinoculation showed significantly more vesicles secreted from alfalfa infected with incompatible fungus compared with compatible fungus, which in turn was more than produced by noninfected plants. Thus, the increased frequency of paramural bodies during incompatible interactions correlated with an increase in EV quantity in apoplastic wash fluids. Together, these results suggest that EVs and paramural bodies contribute to immunity during pathogen attack in alfalfa. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .