Highly aligned electroactive ultrafine fibers promote the differentiation of mesenchymal stem cells into Schwann-like cells for nerve regeneration.

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.

A deep learning model integrating a wind direction-based dynamic graph network for ozone prediction.

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.

Genome-Wide Identification and Functional Analysis of Gene Family and the Critical Role of in Response to Drought Stress in Cotton.

Cotton is an important natural fiber crop. The 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 gene family of four cotton species was analyzed genome-wide, and the key gene was cloned for functional verification.

Limited Duplication-Based List Scheduling Algorithm for Heterogeneous Computing System.

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.