Venus Flytrap-Inspired Data-Center-Free Fast-Responsive Soft Robots Enabled by 2D Ni3(HITP)2 MOF and Graphite.

The rapid and responsive capabilities of soft robots in perceiving, assessing, and reacting to environmental stimuli are highly valuable. However, many existing soft robots, designed to mimic humans and other higher animals, often rely on data centers for the modulation of mechanoelectrical transduction and electromechanical actuation. This reliance significantly increases system complexity and time delays. Herein, drawing inspiration from Venus flytraps, a soft robot employing a power modulation strategy is presented for active stimulus reaction, eliminating the need for a data center. This robot achieves mechanoelectrical transduction through Ni3(2,3,6,7,10,11-hexaiminotriphenylene)2 (Ni3(HITP)2) metal-organic framework (MOF) with an ultralow time delay (256 ns) and electromechanical actuation via graphite. The Joule heating effect in graphite is effectively modulated by Ni3(HITP)2 before and after the presence of pressure, thus enabling the stimulus reaction of soft robots. As demonstrated, three soft robots are created: low-level edge tongue robots, Venus flytrap robots, and high-level nerve-center-controlled dragonfly robots. This power modulation strategy inspires designs of edge soft robots and high-level robots with a human-like effective fusion of conditioned and unconditioned reflexes.

Gamification of Behavior Change: Mathematical Principle and Proof-of-Concept Study.

BACKGROUND: Many people want to build good habits to become healthier, live longer, or become happier but struggle to change their behavior. Gamification can make behavior change easier by awarding points for the desired behavior and deducting points for its omission. OBJECTIVE: In this study, we introduced a principled mathematical method for determining how many points should be awarded or deducted for the enactment or omission of the desired behavior, depending on when and how often the person has succeeded versus failed to enact it in the past. We called this approach optimized gamification of behavior change. METHODS: As a proof of concept, we designed a chatbot that applies our optimized gamification method to help people build healthy water-drinking habits. We evaluated the effectiveness of this gamified intervention in a 40-day field experiment with 1 experimental group (n=43) that used the chatbot with optimized gamification and 2 active control groups for which the chatbot's optimized gamification feature was disabled. For the first control group (n=48), all other features were available, including verbal feedback. The second control group (n=51) received no feedback or reminders. We measured the strength of all participants' water-drinking habits before, during, and after the intervention using the Self-Report Habit Index and by asking participants on how many days of the previous week they enacted the desired habit. In addition, all participants provided daily reports on whether they enacted their water-drinking intention that day. RESULTS: A Poisson regression analysis revealed that, during the intervention, users who received feedback based on optimized gamification enacted the desired behavior more often (mean 14.71, SD 6.57 times) than the active (mean 11.64, SD 6.38 times; P<.001; incidence rate ratio=0.80, 95% CI 0.71-0.91) or passive (mean 11.64, SD 5.43 times; P=.001; incidence rate ratio=0.78, 95% CI 0.69-0.89) control groups. The Self-Report Habit Index score significantly increased in all conditions (P<.001 in all cases) but did not differ between the experimental and control conditions (P>.11 in all cases). After the intervention, the experimental group performed the desired behavior as often as the 2 control groups (P≥.17 in all cases). CONCLUSIONS: Our findings suggest that optimized gamification can be used to make digital behavior change interventions more effective. TRIAL REGISTRATION: Open Science Framework (OSF) H7JN8; https://osf.io/h7jn8.

Quantitative multi-parameter assessment of age- and gender-related variation of back extensor muscles in healthy adults using Dixon MR imaging.

OBJECTIVES: Investigate sex differences in age-related back extensor muscle degeneration using Dixon MRI and analyze the relationship between quantitative muscle parameters and back muscle strength in healthy adults. METHODS: 105 healthy subjects underwent lumbar Dixon MRI. Fat fraction (FF), cross-sectional area (CSA), functional CSA (FCSA), and relative FCSA (RFCSA) of multifidus muscle (MF) and erector spinae (ES) were quantified. Back extension muscle strength was measured using an external fixation dynamometer. ANOVA with post hoc Tukey correction was used for age group comparisons. Partial and Spearman's correlation analyzed relationships between age, muscle parameters, and muscle strength. RESULTS: MF and ES FF significantly increased with age in both genders (r = 0.55-0.85; p < 0.001). Muscle FF increased prominently for females (40-49 years, MF and 50-59 years, ES) and males (60-73 years, MF and ES). In females, total ES FCSA and RFCSA (r = - 0.42, - 0.37; p < 0.01) correlated with age. While in males, all MF and ES muscle size parameters, except total MF CSA, correlated with age (r = - 0.30 to - 0.58; p < 0.05). Back extension muscle strength correlated with mean FF, total CSA, and total FCAS for MF and ES individually (p < 0.001). The combined MF + ES FCSA correlation coefficient (r = 0.63) was higher than FF (r = - 0.51) and CSA (r = 0.59) (p < 0.001). CONCLUSIONS: Age-related back extensor muscle degeneration varies by muscle type and sex. FCSA has the highest association with back muscle strength compared to FF and CSA. CLINICAL RELEVANCE STATEMENT: The investigation of sex differences in age-related back extensor muscle degeneration utilizing Dixon imaging may hold significant implications for evaluating spine health and enabling earlier intervention. Muscles' FCSA could contribute to acquiring additional evidence for reflecting muscle function change. KEY POINTS: • The multifidus muscle (MF) and erector spinae (ES) fat fraction (FF) increased with age at all lumbar disc levels in females and males. • Age-related changes in muscle morphological quantitative parameters of healthy adults were specific by muscle type and gender. • The muscle functional cross-sectional area (FCSA) measured by Dixon imaging may better monitor back extensor muscle strength changes than muscle FF and cross-sectional area (CSA).

Bioinspired Young's Modulus-Hierarchical E-Skin with Decoupling Multimodality and Neuromorphic Encoding Outputs to Biosystems.

As key interfaces for the disabled, optimal prosthetics should elicit natural sensations of skin touch or proprioception, by unambiguously delivering the multimodal signals acquired by the prosthetics to the nervous system, which still remains challenging. Here, a bioinspired temperature-pressure electronic skin with decoupling capability (TPD e-skin), inspired by the high-low modulus hierarchical structure of human skin, is developed to restore such functionality. Due to the bionic dual-state amplifying microstructure and contact resistance modulation, the MXene TPD e-skin exhibits high sensitivity over a wide pressure range and excellent temperature insensitivity (91.2% reduction). Additionally, the high-low modulus structural configuration enables the pressure insensitivity of the thermistor. Furthermore, a neural model is proposed to neutrally code the temperature-pressure signals into three types of nerve-acceptable frequency signals, corresponding to thermoreceptors, slow-adapting receptors, and fast-adapting receptors. Four operational states in the time domain are also distinguished after the neural coding in the frequency domain. Besides, a brain-like machine learning-based fusion process for frequency signals is also constructed to analyze the frequency pattern and achieve object recognition with a high accuracy of 98.7%. The TPD neural system offers promising potential to enable advanced prosthetic devices with the capability of multimodality-decoupling sensing and deep neural integration.

Naphthalene Diimide-Based Donor-Acceptor-Donor Small Molecules as Metal-Free Organocatalysts for Photocatalytic CO2 Reaction.

The application of organic small molecules as metal-free photocatalysts for light-driven photoreduction of carbon dioxide (CO2) has seldom been explored. This work developed four naphthalene diimide (NDI)-derived donor-acceptor-donor small molecules with different numbers of thiophene units, namely, NDI-2T, NDI-TT, NDI-4T, and NDI-6T, as metal-free photocatalysts to catalyze the reduction of CO2 under irradiation with an air mass 1.5G solar simulator at one-sun intensity. The structure-property relationship was investigated by exploring the effects of the electron-donating ability of the donor units on the optical properties, redox potential, electron-hole distribution, and exciton lifetime. NDI-6T exhibited the most red-shifted absorption, longest exciton lifetime, and strongest electron-hole separation. However, the large upshift in oxidation potential because of the elevated electron-donating ability of the hexathiophene unit significantly reduced the driving force for catalyst regeneration, leading to poor catalytic performance. Alternatively, NDI-4T possessed proper redox potentials, reduced charge-transfer resistance, and excellent photocurrent intensity; therefore, it effectively converted CO2 to a single product of CO in the presence of water as an electron donor without a sacrificial reagent or cocatalyst with a product yield of 168.6 µmol gcat-1 24 h-1, which was considerably higher than those of NDI-TT (111.9 µmol gcat-1 24 h-1), NDI-2T (88.4 µmol gcat-1 24 h-1), and NDI-6T (40.5 µmol gcat-1 24 h-1). This study provides a practical guideline for the molecular design of conjugated organic molecules as promising photocatalysts for CO2 photoreduction.

Quantitative MRI evaluation of articular cartilage in patients with meniscus tear.

Purpose: The aim of this study was to assess quantitatively articular cartilage volume, thickness, and T2 value alterations in meniscus tear patients. Materials and methods: The study included 32 patients with meniscus tears (17 females, 15 males; mean age: 40.16 ± 11.85 years) and 24 healthy controls (12 females; 12 males; mean age: 36 ± 9.14 years). All subjects were examined by 3 T magnetic resonance imaging (MRI) with 3D dual-echo steady-state (DESS) and T2 mapping images. All patients underwent diagnostic arthroscopy and treatment. Cartilage thickness, cartilage volume and T2 values of 21 subregions of knee cartilage were measured using the prototype KneeCaP software (version 2.1; Siemens Healthcare, Erlangen, Germany). Mann-Whitney-U tests were utilized to determine if there were any significant differences among subregional articular cartilage volume, thickness and T2 value between patients with meniscus tear and the control group. Results: The articular cartilage T2 values in all subregions of the femur and tibia in the meniscus tear group were significantly higher (p< 0.05) than in the healthy control group. The cartilage thickness of the femoral condyle medial, femur trochlea, femur condyle lateral central, tibia plateau medial anterior and patella facet medial inferior in the meniscus tear group were slightly higher than in the control group (p< 0.05). In the femur trochlea medial, patella facet medial inferior, tibia plateau lateral posterior and tibia plateau lateral central, there were significant differences in relative cartilage volume percentage between the meniscus tear group and the healthy control group (p< 0.05). Nineteen patients had no cartilage abnormalities (Grade 0) in the meniscus tear group, as confirmed by arthroscopic surgery, and their T2 values in most subregions were significantly higher (p< 0.05) than those of the healthy control group. Conclusion: The difference in articular cartilage indexes between patients with meniscus tears and healthy people without such tears can be detected by using quantitative MRI. Quantitative T2 values enable early and sensitive detection of early cartilage lesions.