When synthetic non-biodegradable fishing nets are lost, abandoned or discarded at sea, they may continue to catch fish and other animals for a long period of time. This phenomenon is known as 'ghost fishing'. Biodegradable fishing nets, on the other hand, are intended to degrade or decompose after a certain period of time under water and thereby lose their ghost fishing capacity more quickly than conventional gear. A biodegradable net material, a blend of 82% polybutylene succinate (PBS) and 18% polybutylene adipate-co-terephthalate (PBAT), was developed. We examined the physical properties and degradability of the biodegradable monofilament, and compared the fishing performance of driftnets made of conventional nylon and of the biodegradable material. When dry, conventional nylon monofilament exhibited a greater breaking strength and elongation than biodegradable monofilament of the same diameter. When wet, the biodegradable monofilament exhibited a stiffness of c. 1.5-fold than nylon monofilament. This suggests that a net made of the less flexible biodegradable monofilament would have lower fishing efficiency than conventional nets. The fishing performance comparisons between the biodegradable and conventional nylon nets, however, revealed similar catch rates for yellow croaker Larimichthys polyactis. Biodegradable monofilament started to degrade after 24 months in seawater by marine organisms. We conclude that biodegradable netting may become a feasible alternative to conventional nylon netting and can contribute to reducing the duration of ghost fishing. Nonetheless, there remain many uncertainties, challenges and knowledge gaps that have to be solved before we are able to draw firm conclusions about the overall benefits of these materials in driftnet fisheries.
Clear thermoplastic retainers have been widely used in daily orthodontics; however, they have inherent limitations associated with thermoplastic polymer materials such as dimensional instability, low strength, and poor wear resistance. To solve these problems, we developed a new type of clear orthodontic retainer that incorporates multi-layer hybrid materials. It consists of three layers; an outer polyethylenterephthalate glycol modified (PETG) hard-type polymer, a middle thermoplastic polyurethane (TPU) soft-type polymer, and an inner reinforced resin core. The resin core improves wear resistance and mechanical strength, which prevent unwanted distortion of the bucco-palatal wall of the retainer. The TPU layer absorbs impact and the PETG layer has good formability, optical qualities, fatigue resistance, and dimensional stability, which contributes to increased support from the mandibular dentition, and helps maintain the archform. This new type of vacuum-formed retainer showed improved mechanical strength and rate of water absorption.
Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.
With the rapid technological change of society with Artificial Intelligence, elementary schools' goal should be to prepare the next generations according to competencies. We propose an AI curriculum to cultivate students' AI literacy to answer the question of ‘why and what to teach’ on AI. The proposed AI curriculum focuses on achieving AI literacy based on three competencies: AI Knowledge, AI Skill, and AI Attitude. We anticipate that the proposed curriculum will equip students with core competencies for the future with AI.
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