Highly Efficient and Durable Ammonia Electrolysis Cell Using Zirfon Separator.

Most studies on ammonia electrolysis have focused on anion exchange membranes (AEMs), which face limitations in operating conditions, such as pH and ammonia concentration. This study introduces a novel concept of an ammonia electrolysis cell (AEC) utilizing a Zirfon separator capable of operating under high pH and ammonia concentrations. The Zirfon-based AECs achieve a peak current density of 915 mA cm, representing the highest reported value in AEC literature.

Contrastive learning for enhancing feature extraction in anticancer peptides.

Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts have been made to develop cancer treatments, among which anticancer peptides (ACPs) are garnering recognition for their potential applications. While ACP screening is time-consuming and costly, in silico prediction tools provide a way to overcome these challenges.

Elucidating immunological characteristics of the adenoma-carcinoma sequence in colorectal cancer patients in South Korea using a bioinformatics approach.

Colorectal cancer (CRC) is one of the top five most common and life-threatening malignancies worldwide. Most CRC develops from advanced colorectal adenoma (ACA), a precancerous stage, through the adenoma-carcinoma sequence. However, its underlying mechanisms, including how the tumor microenvironment changes, remain elusive.

Identifying novel antimicrobial peptides from venom gland of spider by deep multi-task learning.

Antimicrobial peptides (AMPs) show promises as valuable compounds for developing therapeutic agents to control the worldwide health threat posed by the increasing prevalence of antibiotic-resistant bacteria. Animal venom can be a useful source for screening AMPs due to its various bioactive components. Here, the deep learning model was developed to predict species-specific antimicrobial activity.

Identification of sp. nov., sp. nov. and sp. nov. and emended description of the genus .

Three chitinolytic, Gram-negative, light pink, capsule-forming, rod-shaped bacterial strains with gliding motion (MYSH2, MJ1a and dk17) were isolated from seashells, soil and foxtail, respectively. Phylogenetic analysis of the 16S rRNA gene sequences and concatenated alignment of 92 core genes indicated that strains MYSH2, MJ1a and dk17 were novel species of the genus and exhibited a high 16S rRNA sequence similarity (i.e.

Designing a Novel Functional Peptide With Dual Antimicrobial and Anti-inflammatory Activities Methods.

As spider venom is composed of various bioactive substances, it can be utilized as a platform for discovering future therapeutics. Host defense peptides are great candidates for developing novel antimicrobial agents due to their multifunctional properties. In this study, novel functional peptides were rationally designed to have dual antibacterial and anti-inflammatory activities with high cytocompatibility.

sp. nov., a carotenoid-producing bacterium isolated from surface seawater.

A Gram-negative, moderately halophilic bacterium, designated as strain Y3S6, was isolated from a surface seawater sample collected from Dongangyoeng cave, Udo-myeon, Jeju-si, Jeju-do, Repulic of Korea. Cells of strain Y3S6 were aerobic, rod-shaped, non-sporulated, yellow, catalase- negative, oxidase-negative and motile with one polar flagellum. Growth of strain Y3S6 occurred at 15-40 °C (optimum: 25-30 °C), at pH 6.

sp. nov. and sp. nov., isolated from Jeongbang Waterfall on Jeju Island.

Two bacterial strains, designated MJB4 and SJ7, were isolated from water samples collected from Jeongbang Falls on Jeju Island, Republic of Korea. Phylogenetic analysis of 16S rRNA gene sequences indicated that the two strains belonged to the genera and , owing to their high similarities to DSM 29641 (97.5 %) and FA042 (96.

A Deep Learning Approach with Data Augmentation to Predict Novel Spider Neurotoxic Peptides.

As major components of spider venoms, neurotoxic peptides exhibit structural diversity, target specificity, and have great pharmaceutical potential. Deep learning may be an alternative to the laborious and time-consuming methods for identifying these peptides. However, the major hurdle in developing a deep learning model is the limited data on neurotoxic peptides.

sp. nov., a carotenoid and flexirubin-type pigment producing species isolated from flooded paddy field.

A Gram-stain-negative, aerobic, asporogenous, motile by gliding, dull-yellow, long rod-shaped bacterial strain, designated SNL9, was isolated from a flooded paddy field near Dongguk University, Republic of Korea. The results of phylogenetic analyses based on 16S rRNA gene sequences indicated that SNL9 represents a member of the genus and is most closely related to DS-12 (96.2%) and YIM 102796 (96.

sp. nov., a fibrillar matrix-producing bacterium isolated from the rhizosphere of rice plant.

A novel fibrillar matrix-producing, rod-shaped, red-orange, asporogenous, aerobic bacterium, designated DK36, was isolated from roots of a rice plant in the Ilsan region near Dongguk University, South Korea. Cells of strain DK36 were Gram-stain-negative and motile by means of gliding. The temperature and pH ranges for growth were 7-35 °C (optimum: 30 °C) and pH 5-10 (optimum: pH 7.

sp. nov., a gamma radiation-resistant bacterium isolated from gamma ray-irradiated soil.

A gamma radiation-resistant and pink-pigmented bacterial strain, designated as 17Sr1-39, was isolated from a gamma ray-irradiated soil sample collected in the Republic of Korea. Cells were Gram-stain-negative, strictly aerobic, flagellated, asporogenous, rod-shaped and methylotrophic. Results of 16S rRNA gene sequence analysis showed that strain 17Sr1-39 was phylogenetically related to PR1016A (97.

Integrative genomic and transcriptomic analysis of genetic markers in Dupuytren's disease.

Background: Dupuytren's disease (DD) is a fibroproliferative disorder characterized by thickening and contracting palmar fascia. The exact pathogenesis of DD remains unknown.

Results: In this study, we identified co-expressed gene set (DD signature) consisting of 753 genes via weighted gene co-expression network analysis.