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This study investigates the efficacy of a novel tissue-engineered scaffold for nerve repair and functional reconstruction following injury. Utilizing stable jet electrospinning, we fabricated aligned ultrafine fibers from dopamine and poly(L-lactic acid) (PLLA), further developing a biomimetic, oriented, and electroactive scaffold comprising poly(pyrrole) (PPy), polydopamine (PDA), and PLLA through dual in situ polymerizations. The scaffold demonstrated enhanced cell adhesion and reactive oxygen species (ROS) scavenging capabilities and promoted the differentiation of mesenchymal stem cells (MSCs) into Schwann-like cells, essential for nerve regeneration. In vivo assessments revealed significant peripheral nerve regeneration in 10 mm sciatic nerve defects in rats, with observations made 12 weeks post-transplantation. This included facilitated myelination and increased muscle density on the injured side, leading to improved motor function recovery. Our results suggest that the aligned PPy/PDA/PLLA fibrous scaffold offers a promising approach for promoting the differentiation of MSCs into Schwann-like cells conducive to nerve regeneration and represents a significant advancement in nerve repair technologies. This study provides a foundational basis for future research into tissue-engineered solutions for nerve damage, potentially impacting clinical strategies for nerve reconstruction.
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Ozone pollution is an important environmental issue in many countries. Accurate forecasting of ozone concentration enables relevant authorities to enact timely policies to mitigate adverse impacts. This study develops a novel hybrid deep learning model, named wind direction-based dynamic spatio-temporal graph network (WDDSTG-Net), for hourly ozone concentration prediction. The model uses a dynamic directed graph structure based on hourly changing wind direction data to capture evolving spatial relationships between air quality monitoring stations. It applied the graph attention mechanism to compute dynamic weights between connected stations, thereby aggregating neighborhood information adaptively. For temporal modeling, it utilized a sequence-to-sequence model with attention mechanism to extract long-range temporal dependencies. Additionally, it integrated meteorological predictions to guide the ozone forecasting. The model achieves a mean absolute error of 6.69 µg/m3 and 18.63 µg/m3 for 1-h prediction and 24-h prediction, outperforming several classic models. The model's IAQI accuracy predictions at all stations are above 75 %, with a maximum of 81.74 %. It also exhibits strong capabilities in predicting severe ozone pollution events, with a 24-h true positive rate of 0.77. Compared to traditional static graph models, WDDSTG-Net demonstrates the importance of incorporating short-term wind fluctuations and transport dynamics for data-driven air quality modeling. In principle, it may serve as an effective data-driven approach for the concentration prediction of other airborne pollutants.
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Cotton is an important natural fiber crop. The RF2 gene family is a member of the bZIP transcription factor superfamily, which plays an important role in plant resistance to environmental stresses. In this paper, the RF2 gene family of four cotton species was analyzed genome-wide, and the key gene RF2-32 was cloned for functional verification. A total of 113 RF2 genes were identified in the four cotton species, and the RF2 family was relatively conserved during the evolution of cotton. Chromosome mapping and collinear analysis indicated that fragment replication was the main expansion mode of RF2 gene family during evolution. Cis-element analysis showed that there were many elements related to light response, hormone response and abiotic stress response in the promoters of RF2 genes. The transcriptome and qRT-PCR analysis of RF2 family genes in upland cotton showed that RF2 family genes responded to salt stress and drought stress. GhRF2-32 protein was localized in the cell nucleus. Silencing the GhRF2-32 gene showed less leaf wilting and increased total antioxidant capacity under drought and salt stress, decreased malondialdehyde content and increased drought and salt tolerance. This study revealed the evolutionary and functional diversity of the RF2 gene family, which laid a foundation for the further study of stress-resistant genes in cotton.
RESUMEN
Efficient scheduling algorithms have been a leading research topic for heterogeneous computing systems. Although duplication-based scheduling algorithms can significantly reduce the total completion time, they are generally accompanied by an exorbitant time complexity. In this paper, we propose a new task duplication-based heuristic scheduling algorithm, LDLS, that can reduce the total completion time and maintains a low time complexity. The scheduling procedure of LDLS is composed of three main phases: In the beginning phase, the maximum number of duplications per level and per task is calculated to prevent excessive duplications from blocking regular tasks. In the next phase, the optimistic cost table (OCT) and ranking of tasks are calculated with reference to PEFT. In the final phase, scheduling is conducted based on the ranking, and the duplication of each task is dynamically determined, enabling the duplicated tasks to effectively reduce the start execution time of its successor tasks. Experiments of algorithms on randomly generated graphs and real-world applications indicate that both the scheduling length and the number of better case occurrences of LDLS are better than others.