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The relationship between cumulative ecological risk and adolescent non-suicidal self-injury (NSSI): a moderated mediation model
Abstract Non-suicidal self-injury (NSSI) is a prominent mental health issue among adolescents. Cumulative ecological risk, defined as concurrent stressors across family, school, and peer systems, has been identified as a key risk factor. However, the underlying mechanisms and protective factors in this relationship have not been fully explored. This study explores the longitudinal relationship between cumulative ecological risk and NSSI, focusing on the serial mediating roles of self-compassion and depressive symptoms, as well as the moderating role of positive stress beliefs. Participants were 742 Chinese adolescents (52.7% female; Mage at Wave 1 = 13.4 years) from a two-wave longitudinal study with data spanning 1 year. Results indicated that cumulative ecological risk indirectly influenced NSSI through the sequential pathway of self-compassion followed by depressive symptoms. Furthermore, positive stress beliefs functioned as a conditional protective factor: they amplified the protective effect of self-compassion on NSSI, and they attenuated the positive association between depressive symptoms and NSSI. This study elucidates the possible mechanisms from cumulative ecological risk to adolescent NSSI, and identifies positive stress beliefs as a protective factor, offering implications for prevention and intervention. Directions for future research are also discussed.
Opportunities to Combat the Chronic Pain–Opioid Use Disorder Syndemic
Microgeographic variation in advertisement call and calling site among color morphs of the polytypic poison frog Oophaga histrionica (Dendrobatidae)
Operative versus Nonoperative Management for Appendicitis
Optimizing lung cancer diagnosis using improved fungal growth optimizer-based medical image segmentation
Abstract Medical image segmentation is one of the most important processes in computer-aided diagnosis. It plays a critical role in detecting and analyzing diseases since it isolates the region of interest in medical scans. Out of many techniques, multilevel thresholding is capable of segmenting complex images. It isolates important structures with multiple intensity thresholds. The effectiveness of multilevel thresholding depends on the optimization algorithm’s capacity to identify the appropriate threshold levels. This paper proposes an effective Fungal Growth Optimizer (FGO) with Orthogonal Learning Strategy (OLS). We emphasize enhancing solution diversity and speeding up convergence in multilevel thresholding-based medical image segmentation. The proposed algorithm, referred to as OLFGO, is tested on a set of 20 chest CT images from an openly available lung cancer database. We use various threshold values (2, 4, 6, 8, 10, and 12) to test its performance at different levels of segmentation. To assess the segmentation performance, we use several evaluation criteria such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Feature Similarity Index Measure (FSIM), Universal Quality Index (UQI) and Dice coefficient (DICE). Moreover, the results are compared with deep learning algorithm’s outcomes. We have demonstrated the reliability and efficacy of the OLFGO algorithm after implementing OLS using the Congress on Evolutionary Computation (CEC 2022) benchmarks. The improvement significantly enhanced the performance of the algorithm. The experiments confirm the efficiency of orthogonal learning in enforcing FGO’s ability towards effective and reliable medical image segmentation. We also employed the Friedman rank sum test to rank the performance of OLFGO against existing techniques. OLFGO was ranked first, which reconfirms its superior ability in image segmentation. The MATLAB implementation of the proposed OLFGO is available at : https://github.com/shimaa21magdy-sketch/Orthogonal-Learning-Fungal-Growth-Optimizer-for-Lung-CT-Segmentation .
Angiography-Based Physiology to Guide Coronary Revascularization
Miniaturized wireless bioelectronics for electrically driven biohybrid robots
Abstract Although biohybrid robots offer the potential for soft, adaptive actuation by harnessing living muscle, practical operation in cell culture environments is often limited by the requirement of immersed leads or cumbersome stimulation equipment. Here, we present a thin, miniaturized, wireless bioelectronic stimulator that can electrically drive biohybrid robots while maintaining stability in aqueous cell culture media. Built on a 50-µm liquid crystal polymer (LCP) substrate, the device integrates a planar receiving coil, interconnects, a diode-based rectifier, and a tank capacitor. This enables the device to convert an approximately 4.9-MHz radio-frequency (RF) input into pulsed direct current (DC), which is delivered through integrated stimulation electrodes. The stimulator has a footprint of ~ 32 mm² and a total thickness and mass of ~ 100 μm and ~ 7 mg, respectively. We integrated the stimulator with a nanopatterned carbon nanotube (CNT)/gelatin hydrogel fin seeded with human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to generate propulsion through fin flapping. By optimizing the thickness of the polydimethylsiloxane (PDMS) encapsulation layer, the density was tuned, and the robot remained freely floating and retained shape integrity during operation. This produced autonomous forward locomotion of 74.8 ± 16.4 μm s − 1 . The stimulator generated distance-dependent output voltage pulses and enabled external pacing/modulation under the tested conditions, without a marked loss of cardiomyocyte attachment or α-actinin-positive sarcomeric organization. Together, these results provide a proof-of-concept compact, media-compatible, wireless bioelectronic interface toward closed-system biohybrid robotics.
What’s the human cost of US research turmoil? A new film finds out
The Invisible Load of Cognitive Symptoms
Courtship vocalizations of male mice do not always attract females
Abstract Male house mice ( Mus musculus ) emit ultrasonic vocalizations (USVs) during courtship and mating, which influence their copulatory success. Several studies have found that playbacks of male USVs attract females, though this response may depend on the presence of male scent, and one study suggested that females are more attracted to complex than simple USV types. We conducted three playback experiments with wild-derived female house mice to test these predictions. None of our results met our expectations: First, females were not more attracted to sequences of complex versus simple USVs. Second, females were also not more attracted to these syllables when compared to silence. Third, females showed no attraction towards more natural sequences containing simple and complex USVs compared to silence, even in the presence of male scent. Thus, we found no evidence that wild-derived female mice show approach behavior towards male USVs, regardless of their complexity or exposure to male olfactory stimuli. These findings raise questions about the repeatability and generalizability of playback studies in house mice and highlight the need for more studies aimed at determining the types of vocalizations and contexts under which mice do and do not show attraction or other responses to USVs.