Background: Foodborne diseases, as a type of disease with a high global incidence, place a heavy burden on public health and social economy. Foodborne pathogens, as the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases; however, foodborne diseases caused by different pathogens lack specificity in clinical features, and there is a low proportion of clinically actual pathogen detection in real life. Objective: We aimed to analyze foodborne disease case data, select appropriate features based on analysis results, and use machine learning methods to classify foodborne disease pathogens to predict foodborne disease pathogens that have not been tested. Methods: We extracted features such as space, time, and exposed food from foodborne disease case data and analyzed the relationship between these features and the foodborne disease pathogens using a variety of machine learning methods to classify foodborne disease pathogens. We compared the results of 4 models to obtain the pathogen prediction model with the highest accuracy. Results: The gradient boost decision tree model obtained the highest accuracy, with accuracy approaching 69% in identifying 4 pathogens including Salmonella, Norovirus, Escherichia coli, and Vibrio parahaemolyticus. By evaluating the importance of features such as time of illness, geographical longitude and latitude, and diarrhea frequency, we found that they play important roles in classifying the foodborne disease pathogens. Conclusions: Data analysis can reflect the distribution of some features of foodborne diseases and the relationship among the features. The classification of pathogens based on the analysis results and machine learning methods can provide beneficial support for clinical auxiliary diagnosis and treatment of foodborne diseases.

The peer merit review of research proposals has been the major mechanism to decide grant awards. However, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign interdisciplinary proposals to appropriate reviewers so proposals are fairly evaluated. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposal-reviewer matching. Existing systems mainly collect topic labels manually generated by principle investigators. However, such human-reported labels can be non-accurate, incomplete, labor intensive, and time costly. What role can AI play in developing a fair and precise proposal reviewer assignment system? In this study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs to learn representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

The China National Center for Food Safety Risk Assessment (CFSA) uses the Foodborne Disease Monitoring and Reporting System (FDMRS) to monitor outbreaks of foodborne diseases across the country. However, there are problems of underreporting or erroneous reporting in FDMRS, which significantly increase the cost of related epidemic investigations. To solve this problem, we designed a model to identify suspected outbreaks from the data generated by the FDMRS of CFSA. In this study, machine learning models were used to fit the data. The recall rate and F1-score were used as evaluation metrics to compare the classification performance of each model. Feature importance and pathogenic factors were identified and analyzed using tree-based and gradient boosting models. Three real foodborne disease outbreaks were then used to evaluate the best performing model. Furthermore, the SHapley Additive exPlanation value was used to identify the effect of features. Among all machine learning classification models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance, with the highest recall rate and F1-score of 0.9699 and 0.9582, respectively. In terms of model validation, the model provides a correct judgment of real outbreaks. In the feature importance analysis with the XGBoost model, the health status of the other people with the same exposure has the highest weight, reaching 0.65. The machine learning model built in this study exhibits high accuracy in recognizing foodborne disease outbreaks, thus reducing the manual burden for medical staff. The model helped us identify the confounding factors of foodborne disease outbreaks. Attention should be paid not only to the health status of those with the same exposure but also to the similarity of the cases in time and space.

CO₂ electroreduction has garnered significant attention from both the academic and industrial communities. Extracting crucial information related to catalysts from domain literature can help scientists find new and effective electrocatalysts. Herein, we used various advanced machine learning, natural language processing techniques and large language models (LLMs) approaches to extract relevant information about the CO₂ electrocatalytic reduction process from scientific literature. By applying the extraction pipeline, we present an open-source corpus for electrocatalytic CO₂ reduction. The database contains two types of corpus: (1) the benchmark corpus, which is a collection of 6,985 records extracted from 1,081 publications by catalysis postgraduates; and (2) the extended corpus, which consists of content extracted from 5,941 documents using traditional NLP techniques and LLMs techniques. The Extended Corpus I and II contain 77,016 and 30,283 records, respectively. Furthermore, several domain literature fine-tuned LLMs were developed. Overall, this work will contribute to the exploration of new and effective electrocatalysts by leveraging information from domain literature using cutting-edge computer techniques.

The field of catalysis holds paramount importance in shaping the trajectory of sustainable development, prompting intensive research efforts to leverage artificial intelligence (AI) in catalyst design. Presently, the fine-tuning of open-source large language models (LLMs) has yielded significant breakthroughs across various domains such as biology and healthcare. Drawing inspiration from these advancements, we introduce CataLM (Catalytic Language Model), a large language model tailored to the domain of electrocatalytic materials. Our findings demonstrate that CataLM exhibits remarkable potential for facilitating human-AI collaboration in catalyst knowledge exploration and design. To the best of our knowledge, CataLM stands as the pioneering LLM dedicated to the catalyst domain, offering novel avenues for catalyst discovery and development.