Bioinspired materials for underwater adhesion with pathways to switchability

Strong adherence to underwater or wet surfaces for applications like tissue adhesion and underwater robotics is a significant challenge. This is especially apparent when switchable adhesion is required that demands rapid attachment, high adhesive capacity, and easy release. Nature displays a spectrum of permanent to reversible attachment from organisms ranging from the mussel to the octopus, providing inspiration for underwater adhesion design that has yet to be fully leveraged in synthetic systems. Here, we review the challenges and opportunities for creating underwater adhesives with a pathway to switchability. We discuss key material, geometric, modeling, and design tools necessary to achieve underwater adhesion similar to the adhesion control demonstrated in nature. Through these interdisciplinary efforts, we envision that bioinspired adhesives can rise to or even surpass the extraordinary capabilities found in biological systems

Octopus-inspired adhesive skins for intelligent and rapidly switchable underwater adhesion

The octopus couples controllable adhesives with intricately embedded sensing, processing, and control to manipulate underwater objects. Current synthetic adhesive–based manipulators are typically manually operated without sensing or control and can be slow to activate and release adhesion, which limits system-level manipulation. Here, we couple switchable, octopus-inspired adhesives with embedded sensing, processing, and control for robust underwater manipulation. Adhesion strength is switched over 450× from the ON to OFF state in \u3c50 ms over many cycles with an actively controlled membrane. Systematic design of adhesive geometry enables adherence to nonideal surfaces with low preload and independent control of adhesive strength and adhesive toughness for strong and reliable attachment and easy release. Our bio-inspired nervous system detects objects and autonomously triggers the switchable adhesives. This is implemented into a wearable glove where an array of adhesives and sensors creates a biomimetic adhesive skin to manipulate diverse underwater objects

The Effects of Body Composition Characteristics on the Functional Disability in Patients with Degenerative Lumbar Spinal Stenosis

Several research studies suggest that obese patients are at a higher risk of developing lumbar spinal disorder, including degenerative lumbar spinal stenosis (LSS), compared to normal-weight individuals. However, there are few investigations of how obesity affects functional disability in activities of daily living (ADL) in patients who were diagnosed with LSS. This prospective observational study aimed to determine if an association exists between body composition parameters, such as body fat and skeletal muscle, and functional disability in ADL of LSS patients. In the results of the current study, there were significant differences in percent body fat between the mild/moderate and severe disability groups. However, there were no differences in skeletal muscle mass or index between the two groups. Furthermore, we found a positive linear relationship between percent body fat and functional disability in male sex. This study suggests that increased percent body fat predicts potential severe functional disability in ADL in LSS patients. Body composition analysis may provide useful information for predicting the disease severity of various lumbar spinal disorders in clinical practice

Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation

Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided

FtsH2-Dependent Proteolysis of EXECUTER1 Is Essential in Mediating Singlet Oxygen-Triggered Retrograde Signaling in Arabidopsis thaliana

Photosystem II reaction center (PSII RC) and light-harvesting complex inevitably generate highly reactive singlet oxygen (1O2) that can impose photo-oxidative damage, especially when the rate of generation exceeds the rate of detoxification. Besides being toxic, 1O2 has also been ascribed to trigger retrograde signaling, which leads to nuclear gene expression changes. Two distinctive molecular components appear to regulate 1O2 signaling: a volatile signaling molecule β-cyclocitral (β-CC) generated upon oxidation of β-carotene by 1O2 in PSII RC assembled in grana core, and a thylakoid membrane-bound FtsH2 metalloprotease that promotes 1O2-triggered signaling through the proteolysis of EXECUTER1 (EX1) proteins associated with PSII in grana margin. The role of FtsH2 protease in 1O2 signaling was established recently in the conditional fluorescent (flu) mutant of Arabidopsis thaliana that generates 1O2 upon dark-to-light shift. The flu mutant lacking functional FtsH2 significantly impairs 1O2-triggered and EX1-mediated cell death. In the present study, the role of FtsH2 in the induction of 1O2 signaling was further clarified by analyzing the FtsH2-dependent nuclear gene expression changes in the flu mutant. Genome-wide transcriptome analysis showed that the inactivation of FtsH2 repressed the majority (85%) of the EX1-dependent 1O2-responsive genes (SORGs), providing direct connection between FtsH2-mediated EX1 degradation and 1O2-triggered gene expression changes. Furthermore, the overlap between β-CC-induced genes and EX1-FtsH2-dependent genes was very limited, further supporting the coexistence of two distinctive 1O2 signaling pathways