Owing to blockchain characteristics such as transparency, traceability, and disintermediation, blockchain technology has been widely employed in sustainable supply chain management. The COVID-19 pandemic has accelerated the use of blockchain technology in the supply chain. Although most companies have realized the importance of blockchain technology, they often lack understanding of how to plan, measure, cultivate, and improve their own blockchain operation capabilities. Academic research has insufficiently explored the connotations and internal structure of blockchain operation capabilities and does not provide a clear understanding of how to transform blockchain operation capabilities to produce effective performance. In this context, we proposed a concept of blockchain operation capabilities for first time. We took the perspectives of the resource-based view and sociomaterialism theory, based on IT capabilities, big data analysis capabilities, and existing blockchain supply chain research, and explored the relationship between blockchain operation capabilities and competitive performance. We then constructed a hierarchical model for blockchain operation capabilities. To test our proposed research model, we used an online survey to collect data from 1206 firm managers with blockchain technology supply chain experience. The results showed that blockchain operation capabilities has a positive impact on supply chain integration and competitive performance, while supply chain integration has a strong mediating effect on the blockchain operation capabilities and competitive performance relationship. Implications for research and practice are discussed.

Background: G-protein coupled receptors (GPCRs) sense and transmit extracellular signals into the intracellular machinery by regulating G proteins. GPCR malfunctions are associated with a variety of signaling-related diseases, including cancer and diabetes; at least a third of the marketed drugs target GPCRs. Thus, characterization of their signaling and regulatory mechanisms is crucial for the development of effective drugs. Results: In this study, we developed a machine learning model to identify GPCR agonists and antagonists. We designed two-step prediction models: the first model identified the ligands binding to GPCRs and the second model classified the ligands as agonists or antagonists. Using 990 selected subset features from 5270 molecular descriptors calculated from 4590 ligands deposited in two drug databases, our model classified non-ligands, agonists, and antagonists of GPCRs, and achieved an area under the ROC curve (AUC) of 0.795, sensitivity of 0.716, specificity of 0.744, and accuracy of 0.733. In addition, we verified that 70% (44 out of 63) of FDA-approved GPCR-targeting drugs were correctly classified into their respective groups. Conclusions: Studies of ligand–GPCR interaction recognition are important for the characterization of drug action mechanisms. Our GPCR–ligand interaction prediction model can be employed in the pharmaceutical sciences for the efficient virtual screening of putative GPCR-binding agonists and antagonists.

Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms. We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition. Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

This study aimed to evaluate the effects of water temperature and catch frequency on biodiversity and distribution in the waters around Korea. We analyzed spatiotemporal catch data reported by fishermen and evaluated the fluctuations in the dominantly caught species based on the timing of the water temperature changes between 1999 and 2021. This study found an upward trend in the water temperature, and species diversity increased in the East, West, and South Seas after the water temperature changed. Relatively stable species diversity was maintained in the East Sea/Sea of Japan, although variability increased significantly in the West and South Seas. The distribution in the caught species showed that Octopodidae spp. and Ommastrephidae spp. were increasingly caught more consistently alongside Pleuronectidae spp. Moreover, this dominance was maintained even after the change in water temperature and was shown to be relatively less sensitive to water temperature changes.

Climate change is a global phenomenon that significantly impacts the ocean environment around the Korean Peninsula. These changes in climate can lead to rising sea temperatures, thereby significantly affecting marine life and ecosystems in the region. In this study, four statistical approaches were employed to analyze ocean characteristics around the Korean Peninsula: layer classification, imputation for replacing missing values, evaluation using statistical tests, and trend analysis. The trend model we used was a deep learning-based seasonal-trend decomposition using Loess, a piecewise regression module with change points in 2000 and 2009, and Fourier transform to calculate the seasonality of one year. In addition, the water temperature was considered to have a Gaussian distribution so that anomalous water temperatures could be detected through confidence intervals. The ocean was first classified into three layers (surface layer, middle layer, and bottom layer) to characterize the sea area around Korea, after which multiple imputation methods were employed to replace missing values for each layer. The imputation method exhibiting the best performance was then selected by comparing the replaced missing values with high-quality data. Additionally, we compared the slope of the water temperature change around the Korean Peninsula based on two temporal inflection points (2000 and 2009). Our findings demonstrated that the long-term change in water temperature aligns with previous studies. However, the slope of the water temperature change has tended to accelerate since 2009.