The anterior byssal retractor muscle (ABRM) of a bivalve mollusc Mytilus edulis is known to exhibit catch state, i.e. a prolonged tonic contraction maintained with very little energy expenditure. Two different hypotheses have been put forward concerning the catch state; one assumes actin-myosin linkages between the thick and thin filaments that dissociate extremely slowly (linkage hypothesis), while the other postulates a load-bearing structure other than actin-myosin linkages (parallel hypothesis). We explored the possible load-bearing structure responsible for the catch state by examining the arrangement of the thick and thin filaments within the ABRM fibers, using techniques of quick freezing and freeze substitution. No thick filament aggregation was observed in the cross-section of the fibers quickly frozen not only in the relaxed and actively contracting states but also in the catch state. The thick filaments were, however, occasionally interconnected with each other either directly or by distinct projections in all the three states studied. The proportion of the interconnected thick filaments relative to the total thick filaments in a given cross-sectional area was much larger in the catch state than in the relaxed and actively contracting states, providing evidence that the thick filament interconnection is responsible for the catch state.
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http://dx.doi.org/10.1016/s1095-6433(02)00225-8 | DOI Listing |
Sci Rep
January 2025
Centro de Investigação em Biodiversidade e Recursos Genéticos, CIBIO-InBIO, Universidade do Porto, Campus Agrário de Vairão, r/ Padre Armando Quintas, Vairão, 4485-661, Portugal.
Populations of large pelagic sharks are declining worldwide due to overfishing. Determining the overlap between shark populations and fishing activities is important to inform conservation measures. However, for many threatened sharks the whereabouts of particularly vulnerable life-history stages - such as pregnant females and juveniles - are poorly known.
View Article and Find Full Text PDFConscious Cogn
January 2025
Department of Psychological and Brain Sciences, University of California Santa Barbara, CA, USA.
Asking participants to Think Aloud is a common method for studying conscious experience, but it remains unclear whether this approach alters thought qualities-such as meta-awareness, rate of topic shifts, or the content of thoughts in task-absent conditions. To investigate this, we conducted two studies comparing thinking aloud to thinking silently. In Study 1, 111 participants alternated between 15-minute intervals of verbalizing and silently reflecting on their stream of consciousness in a counterbalanced design.
View Article and Find Full Text PDFNature
January 2025
WorldFish, Penang, Malaysia.
Sustainable development aspires to "leave no one behind". Even so, limited attention has been paid to small-scale fisheries (SSF) and their importance in eradicating poverty, hunger and malnutrition. Through a collaborative and multidimensional data-driven approach, we have estimated that SSF provide at least 40% (37.
View Article and Find Full Text PDFPLoS One
January 2025
Laboratorio de Biologia, Controle e Vigilância de Insetos Vetores (LBCVIV), Instituto Oswaldo Cruz (IOC)/ Fiocruz, Rio de Janeiro, Rio de Janeiro, Brazil.
Entomological surveillance plays a crucial role in designing and implementing mosquito control measures. In this context, developing more effective collection strategies is essential to accurately estimate the entomological parameters necessary for effective control. In this study, we investigated the effectiveness of four traps: CDC light trap, MosqTent, BG-Sentinel, and SkeeterVac, compared to human landing catch (HLC) in the collection of Mansonia mosquitoes, known to cause discomfort to riverside populations along the Madeira River in the District of Jaci Paraná, Porto Velho, in Rondônia state, Brazil.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13120, Republic of Korea.
Network security is crucial in today's digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.
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