Social media insights into spatio-temporal emotional responses to COVID-19 crisis.

The Coronavirus pandemic has presented multifaceted challenges in urban emotional well-being and mental health management. Our study presents a spatio-temporal sentiment mining (STSM) framework to address these challenges, focusing on the space-time geography and environmental psychology. This framework analyzes the distribution and trends of 6 categories of public sentiments in Shanghai during the COVID-19 crisis, considering the potential urban spatial influencing factors.

Construction of a Linked Data Set of COVID-19 Knowledge Graphs: Development and Applications.

Background: With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data.

Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study.

Background: Minimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discovering various DDIs.

Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph.

Adverse drug reactions (ADRs) are a major public health concern, and early detection is crucial for drug development and patient safety. Together with the increasing availability of large-scale literature data, machine learning has the potential to predict unknown ADRs from current knowledge. By the machine learning methods, we constructed a Tumor-Biomarker Knowledge Graph (TBKG) which contains four types of node: Tumor, Biomarker, Drug, and ADR using biomedical literatures.

On building a diabetes centric knowledge base via mining the web.

Background: Diabetes has become one of the hot topics in life science researches. To support the analytical procedures, researchers and analysts expend a mass of labor cost to collect experimental data, which is also error-prone. To reduce the cost and to ensure the data quality, there is a growing trend of extracting clinical events in form of knowledge from electronic medical records (EMRs).

[Joint inhibitory effects researches on Microcystis aeruginosa and Chlorella pyrenoidosa of phenolic acids].

Objective: To investigate the inhibitory effects of single and compound phenolic acids on mixed algae.

Methods: Salicylic acid, cinnamic acid and pyrogallic acid were chosen individually or in pairs to act on mixed algae of Microcystis aeruginosa and Chlorella pyrenoidosa.

Results: Three phenolic acids that singled or paired showed certain inhibitory effects on the mixed algae, of which were pyrogallic acid > salicylic acid > cinnamic acid in single phenolic acid, the EC50s of the three phenolic acids on the mixture of Microcystis aeruginosa and Chlorella pyrenoidosa were 7.

[Inhibitory effect of pyrogallic acid on Microcystis aeruginosa and the model analysis].

Objective: To explore the effective agent to inhibit the growth of water-bloom algae and establish corresponding forecast model based on the inhibitory effect.

Methods: The inhibitory effect of pyrogallic acid at different concentrations on Microcystis aeruginosa was studied in the indoor control conditions and the mathematical model of algal growth inhibition was established based on the traditional Logistic model.

Results: (1) Pyrogallic acid has strong power to inhibit the growth of Microcystis aeruginosa, the EC50 of the fifth day was 0.