Fault detection in electromechanical systems plays a significant role in product quality and manufacturing efficiency during the transition to smart manufacturing. Because collecting a sufficient number of datasets under faulty conditions of the system is challenging in practical industrial sites, unsupervised fault detection methods are mainly used. Although fault datasets accumulate during machine operation, it is not straightforward to utilize the information it contains for fault detection after the deep learning model has been trained in an unsupervised manner. However, the information in fault datasets is expected to significantly contribute to fault detection. In this regard, this study aims to validate the effectiveness of the transition from unsupervised to supervised learning as fault datasets gradually accumulate through continuous machine operation. We also focus on experimentally analyzing how changes in the learning paradigm of the deep learning model and the output representation affect fault detection performance. The results demonstrate that, with a small number of fault datasets, a supervised model with continuous outputs as a regression problem showed better fault detection performance than the original model with one-hot encoded outputs (as a classification problem).

The dynastid beetle Allomyrina dichotoma has been used as a herbal medicine. Recently, we performed de novo RNAsequencing of Allomyrina dichotoma and identified several antimicrobial peptide candidates based on in silico analysis.Among them, cationic antimicrobial peptide, Allomyrinasin, was selected and we assessed the anti-inflammatory activitiesof Allomyrinasin against mouse macrophage Raw264.7 cells. The results showed that Allomyrinasin decreased the nitricoxide production of the lipopolysaccharide-induced Raw264.7 cells. In addition, quantitative RT-PCR, ELISA and Westernblot analysis revealed that Allomyrinasin reduced cytokine expression levels in the Raw264.7 cells. Taken together, thesedata indicated that Allomyrinasin had anti-inflammatory activity in the lipopolysaccharide-induced Raw264.7 cells.

We have analyzed the transcriptome of Scolopendra subspinipes mutilans using RNA sequencing and identified severalantimicrobial peptide candidates. Among the peptides, named scolopendrasins, were selected based on the physicochemicalproperties of antimicrobial peptides via an in silico analysis. As a result, we evaluated the antimicrobial activities ofscolopendrasins against Gram positive and negative bacteria including Candida albicans by radial diffusion assay and colonycount assay. We also investigated the cytotoxicity of scolopendrasins through hemolysis assay. We found that the actionof scolopendrasins involved binding to the surface of microorganisms via a specific interaction with lipopolysaccharides,lipoteichoic acid, and peptidoglycans, which are components of the bacterial membrane. These results will provide a basisfor developing therapeutic agents such as peptide antibiotics.

Although the grasshopper Oxya chinensis sinuosa has long been used as a food in Korea, there is little data on itsfunctional effects. Thus we prepared and analyzed total RNA from the whole body of adult Escherichia coli non-immunizedand immunized Oxya chinensis sinuosa strains. Using an Illumina Hiseq sequencer, we generated a total of 66,555 pooledtranscriptome and singletons with and without Escherichia coli immunization, respectively. Then, we performed in silicoanalysis of the Oxya chinensis sinuosa transcriptome, using bioinformatics tools for screening putative antimicrobial peptides(AMPs) and 38 AMPs were finally selected and tested their antimicrobial activity of Gram positive, Gram negative bacteriaand antifungal activity with radial diffusion assay. As a result, 5 out of 38 AMPs showed the highest antimicrobial activityand antifungal activity against microbes and it also evidenced with no hemolytic activity.

The insects have been investigated as novel sources for foods and biomaterials in several recent studies. However,its effects on hair growth have not been sufficiently researched. To develop novel and natural materials for preventingalopecia and promoting hair growth, we investigated the antioxidant activity and hair growth promoting effect of Tenebriomolitor larvae extract (TME). As a result, DPPH radical scavenging activity was 81.17%, and nitrite scavenging activitywas 43.69%, which were similar to blueberry extract. And TME promoted the proliferation of human DPCs and NIH3T3cells, concentration dependently. In addition, the TME prevented not only DHT-induced DPCs cytotoxicity but also actionof TBM as a potassium channel blocker in NIH3T3 cells. These results suggest that TME can be used as a functionalmaterial for alopecia therapeutic reagent by preventing hair loss and promoting hair growth.

We recently reported the in vitro and in vivo antiobesity effects of Tenebrio molitor larvae, a traditional food in manycountries, but it remains unknown how the larvae affect appetite regulation in mice with diet-induced obesity. We hypothesizedthat the extract of T. molitor larvae mediates appetite by regulating neuropeptide expression. We investigated T. molitorlarvae extract's (TME's) effects on anorexigenesis and endoplasmic reticulum (ER) stress–induced orexigenic neuropeptideexpression in the hypothalami of obese mice. Central administration of TME suppressed feeding by down-regulating theexpression of the orexigenic neuropeptides neuropeptide Y and agouti-related protein. T. molitor larvae extract significantlyreduced the expression of ER stress response genes. These results suggest that TME and its bioactive components arepotential therapeutics for obesity and ER stress–driven disease states.