Reliably Filter Drug-Induced Liver Injury Literature With Natural Language Processing and Conformal Prediction

Drug-induced liver injury describes the adverse effects of drugs that damage the liver. Life-threatening results were also reported in severe cases. Therefore, liver toxicity is an important assessment for new drug candidates. These reports are documented in research papers that contain preliminary in vitro and in vivo experiments. Conventionally, data extraction from publications relies on resource-demanding manual labeling, which restricts the efficiency of the information extraction. The development of natural language processing techniques enables the automatic processing of biomedical texts. Herein, based on around 28,000 papers (titles and abstracts) provided by the Critical Assessment of Massive Data Analysis challenge, this study benchmarked model performances on filtering liver-damage-related literature. Among five text embedding techniques, the model using term frequency-inverse document frequency (TF-IDF) and logistic regression outperformed others with an accuracy of 0.957 on the validation set. Furthermore, an ensemble model with similar overall performances was developed with a logistic regression model on the predicted probability given by separate models with different vectorization techniques. The ensemble model achieved a high accuracy of 0.954 and an F1 score of 0.955 in the hold-out validation data in the challenge. Moreover, important words in positive/negative predictions were identified via model interpretation. The prediction reliability was quantified with conformal prediction, which provides users with a control over the prediction uncertainty. Overall, the ensemble model and TF-IDF model reached satisfactory classification results, which can be used by researchers to rapidly filter literature that describes events related to liver injury induced by medications

The Specificity of Human Capital and Risk Management of the College Counselor from the Perspective of Internationalization

Shifting the concept of human resource to the concept of human capital is an inevitable tendency in developing human resource of college counselor. It is because the college counselor has its own specificity that it is hardly possible to avoid the risks of entry and exit which brings in completely. The paper listed the priority of psychological capital, human capital and social capital of the college counselor from the perspective of in-system in the order to attempt to discuss their inner logical relationship based on the basic theory of risk management. Key words: College counsellor; The specificity of human capital; Risk management; In-system Résumé: Déplacer le concept de ressources humaines pour le concept de capital humain est une tendance inévitable dans le développement des ressources humaines de conseiller du collège. C'est parce que le conseiller collège a sa propre spécificité qu'il n'est guère possible d'éviter les risques d'entrée et de sortie qui amène à fond. Le document énumère les priorités du capital psychologique, le capital humain et le capital social de la conseillère collège dans la perspective d'en-système dans l'ordre pour tenter de discuter de leur relation logique interne basé sur la théorie de base de gestion des risques. Mots clés: Université de conseiller; La spécificité du capital humain; La gestion des risques; Et du systèm

Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling

In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge. In this model, the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm, which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems. The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them. This leads to a reduction in wind curtailment rate from 16.3% to 0% and lowers the overall operating cost by 5445.6 Yuan, demonstrating significant economic and environmental benefits.Comment: in Chinese language, Accepted by Electric Power Constructio

Advancing oral delivery of biologics: machine learning predicts peptide stability in the gastrointestinal tract

The oral delivery of peptide therapeutics could facilitate precision treatment of numerous gastrointestinal (GI) and systemic diseases with simple administration for patients. However, the vast majority of licensed peptide drugs are currently administered parenterally due to prohibitive peptide instability in the GI tract. As such, the development of GI-stable peptides is receiving considerable investment. This study provides researchers with the first tool to predict the GI stability of peptide therapeutics based solely on the amino acid sequence. Both unsupervised and supervised machine learning techniques were trained on literature-extracted data describing peptide stability in simulated gastric and small intestinal fluid (SGF and SIF). Based on 109 peptide incubations, classification models for SGF and SIF were developed. The best models utilized k-Nearest Neighbor (for SGF) and XGBoost (for SIF) algorithms, with accuracies of 75.1% (SGF) and 69.3% (SIF), and f1 scores of 84.5% (SGF) and 73.4% (SIF) under 5-fold cross-validation. Feature importance analysis demonstrated that peptides’ lipophilicity, rigidity, and size were key determinants of stability. These models are now available to those working on the development of oral peptide therapeutics

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph classification. However, prior arts on graph representation learning focus on domain specific problems and train a dedicated model for each graph dataset, which is usually non-transferable to out-of-domain data. Inspired by the recent advances in pre-training from natural language processing and computer vision, we design Graph Contrastive Coding (GCC) -- a self-supervised graph neural network pre-training framework -- to capture the universal network topological properties across multiple networks. We design GCC's pre-training task as subgraph instance discrimination in and across networks and leverage contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations. We conduct extensive experiments on three graph learning tasks and ten graph datasets. The results show that GCC pre-trained on a collection of diverse datasets can achieve competitive or better performance to its task-specific and trained-from-scratch counterparts. This suggests that the pre-training and fine-tuning paradigm presents great potential for graph representation learning.Comment: 11 pages, 5 figures, to appear in KDD 2020 proceeding

Modelling and Simulation of Vessel Surgery based on Mass-spring

As the technology developing, precision and accuracy required in medical surgery can be realized through virtual-reality technology in computer aided systems, so that it can satisfy with medical experiments and teaching. In surgery simulation on soft tissues, Mass-spring takes the important roles on simulating the surface transformation of tissues. In this article, we established an intelligent simulation platform for surgery of vein in which includes the transformation based on Mass-spring. This platform can provide good human-computer interface and control some simple motions. It is convenient for medical teaching to instruct the operation scene

Modelling and Simulation of Vessel Surgery based on Mass-spring

As the technology developing, precision and accuracy required in medical surgery can be realized through virtual-reality technology in computer aided systems, so that it can satisfy with medical experiments and teaching. In surgery simulation on soft tissues, Mass-spring takes the important roles on simulating the surface transformation of tissues. In this article, we established an intelligent simulation platform for surgery of vein in which includes the transformation based on Mass-spring. This platform can provide good human-computer interface and control some simple motions. It is convenient for medical teaching to instruct the operation scene

Numerical Simulation of Combustion of Natural Gas Mixed with Hydrogen in Gas Boilers

Hydrogen mixed natural gas for combustion can improve combustion characteristics and reduce carbon emission, which has important engineering application value. A casing swirl burner model is adopted to numerically simulate and research the natural gas hydrogen mixing technology for combustion in gas boilers in this paper. Under the condition of conventional air atmosphere and constant air excess coefficient, the six working conditions for hydrogen mixing proportion into natural gas are designed to explore the combustion characteristics and the laws of pollution emissions. The temperature distributions, composition, and emission of combustion flue gas under various working conditions are analyzed and compared. Further investigation is also conducted for the variation laws of NOx and soot generation. The results show that when the boiler heating power is constant, hydrogen mixing will increase the combustion temperature, accelerate the combustion rate, reduce flue gas and CO2 emission, increase the generation of water vapor, and inhibit the generation of NOx and soot. Under the premise of meeting the fuel interchangeability, it is concluded that the optimal hydrogen mixing volume fraction of gas boilers is 24.7%