A Study on the Growth and Physiological Toxicity Effects of the Combined Exposure of Microplastics and Cadmium on the Vicia faba L. Seedlings.

To investigate the toxicological effects of polystyrene microplastics (PS-MPs), cadmium (Cd), and their combined contamination on the growth and physiological responses of V. faba seedlings, this experiment employed a hydroponic method. The Hoagland nutrient solution served as the control, changes in root growth, physiological and biochemical indicators of V. faba seedlings under different concentrations of PS-MPs (10, 100 mg/L) alone and combined with 0.5 mg/L Cd. The results demonstrated that the root biomass, root vitality, generation rate of superoxide radicals (O2·-), malondialdehyde (MDA) content, and superoxide dismutase (SOD) activity increased with increasing concentration under the influence of PS-MPs alone, while the soluble sugar content and peroxidase (POD) activity decreased. In the combined treatment with Cd, the trends of these indicators are generally similar to the PS-MPs alone treatment group. However, root vitality and SOD activity showed an inverse relationship with the concentration of PS-MPs. Furthermore, laser confocal and electron microscopy scanning revealed that the green fluorescent polystyrene microspheres entered the root tips of the V. faba and underwent agglomeration in the treatment group with a low concentration of PS-MPs alone and a high concentration of composite PS-MPs with Cd.

Knowledge-Based Reasoning Network for Relation Detection.

With the rapid growth of large-scale knowledge bases (KBs), knowledge base question answering (KBQA) has attracted increasing attention recently. Relation detection plays an important role in the KBQA system, which finds a compatible answer by analyzing the semantics of questions and querying and reasoning with multiple KB triples. Significant progress has been made by deep neural networks. However, existing methods often concern on detecting single-hop relation without path reasoning, and a few of these methods exploit the multihop relation reasoning, which involves the answer reasoning from the noisy and abundant relational paths in the KB. Meanwhile, the relatedness between question and answer candidates has received little attention and remains unsolved. This article proposes a novel knowledge-based reasoning network (KRN) for relation detection, including both single-hop relation and multihop relation. To address the semantic gap problem in question-answer interaction, we first learn attentive question representations according to the influence of answer aspects. Then, we learn the single-hop relation sequence through different levels of abstraction and adopt the KB entity and structure information to denoise the multihop relation detection task. Finally, we adopt a Siamese network to measure the similarity between question representation and relation representation for both single-hop and multihop relation KBQA tasks. We conduct experiments on two well-known benchmarks, SimpleQuestions and WebQSP, and the results show the superiority of our approach over the state-of-the-art models for both single-hop and multihop relation detection. Our model also achieves a significant improvement over existing methods on KBQA end task. Further analysis demonstrates the robustness and the applicability of the proposed approach.

Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier.

BACKGROUND: Ontology has attracted substantial attention from both academia and industry. Handling uncertainty reasoning is important in researching ontology. For example, when a patient is suffering from cirrhosis, the appearance of abdominal vein varices is four times more likely than the presence of bitter taste. Such medical knowledge is crucial for decision-making in various medical applications but is missing from existing medical ontologies. In this paper, we aim to discover medical knowledge probabilities from electronic medical record (EMR) texts to enrich ontologies. First, we build an ontology by identifying meaningful entity mentions from EMRs. Then, we propose a symptom-dependency-aware naïve Bayes classifier (SDNB) that is based on the assumption that there is a level of dependency among symptoms. To ensure the accuracy of the diagnostic classification, we incorporate the probability of a disease into the ontology via innovative approaches. RESULTS: We conduct a series of experiments to evaluate whether the proposed method can discover meaningful and accurate probabilities for medical knowledge. Based on over 30,000 deidentified medical records, we explore 336 abdominal diseases and 81 related symptoms. Among these 336 gastrointestinal diseases, the probabilities of 31 diseases are obtained via our method. These 31 probabilities of diseases and 189 conditional probabilities between diseases and the symptoms are added into the generated ontology. CONCLUSION: In this paper, we propose a medical knowledge probability discovery method that is based on the analysis and extraction of EMR text data for enriching a medical ontology with probability information. The experimental results demonstrate that the proposed method can effectively identify accurate medical knowledge probability information from EMR data. In addition, the proposed method can efficiently and accurately calculate the probability of a patient suffering from a specified disease, thereby demonstrating the advantage of combining an ontology and a symptom-dependency-aware naïve Bayes classifier.

KMR: knowledge-oriented medicine representation learning for drug-drug interaction and similarity computation.

Efficient representations of drugs provide important support for healthcare analytics, such as drug-drug interaction (DDI) prediction and drug-drug similarity (DDS) computation. However, incomplete annotated data and drug feature sparseness create substantial barriers for drug representation learning, making it difficult to accurately identify new drug properties prior to public release. To alleviate these deficiencies, we propose KMR, a knowledge-oriented feature-driven method which can learn drug related knowledge with an accurate representation. We conduct series of experiments on real-world medical datasets to demonstrate that KMR is capable of drug representation learning. KMR can support to discover meaningful DDI with an accuracy rate of 92.19%, demonstrating that techniques developed in KMR significantly improve the prediction quality for new drugs not seen at training. Experimental results also indicate that KMR can identify DDS with an accuracy rate of 88.7% by facilitating drug knowledge, outperforming existing state-of-the-art drug similarity measures.

CBN: Constructing a clinical Bayesian network based on data from the electronic medical record.

The process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on knowledge bases such as ontology that are manually compiled through a labour-intensive process or automatically derived using simple pairwise statistics. We explore CBN, a Clinical Bayesian Network construction for medical ontology probabilistic inference, to learn high-quality Bayesian topology and complete ontology directly from EMRs. Specifically, we first extract medical entity relationships from over 10,000 deidentified patient records and adopt the odds ratio (OR value) calculation and the K2 greedy algorithm to automatically construct a Bayesian topology. Then, Bayesian estimation is used for the probability distribution. Finally, we employ a Bayesian network to complete the causal relationship and probability distribution of ontology to enhance the ontology inference capability. By evaluating the learned topology versus the expert opinions of physicians and entropy calculations and by calculating the ontology-based diagnosis classification, our study demonstrates that the direct and automated construction of a high-quality health topology and ontology from medical records is feasible. Our results are reproducible, and we will release the source code and CN-Stroke knowledge graph of this work after publication.1.

An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription.

BACKGROUND: The available antibiotic decision-making systems were developed from a physician's perspective. However, because infectious diseases are common, many patients desire access to knowledge via a search engine. Although the use of antibiotics should, in principle, be subject to a doctor's advice, many patients take them without authorization, and some people cannot easily or rapidly consult a doctor. In such cases, a reliable antibiotic prescription support system is needed. METHODS AND RESULTS: This study describes the construction and optimization of the sensitivity and specificity of a decision support system named IDDAP, which is based on ontologies for infectious disease diagnosis and antibiotic therapy. The ontology for this system was constructed by collecting existing ontologies associated with infectious diseases, syndromes, bacteria and drugs into the ontology's hierarchical conceptual schema. First, IDDAP identifies a potential infectious disease based on a patient's self-described disease state. Then, the system searches for and proposes an appropriate antibiotic therapy specifically adapted to the patient based on factors such as the patient's body temperature, infection sites, symptoms/signs, complications, antibacterial spectrum, contraindications, drug-drug interactions between the proposed therapy and previously prescribed medication, and the route of therapy administration. The constructed domain ontology contains 1,267,004 classes, 7,608,725 axioms, and 1,266,993 members of "SubClassOf" that pertain to infectious diseases, bacteria, syndromes, anti-bacterial drugs and other relevant components. The system includes 507 infectious diseases and their therapy methods in combination with 332 different infection sites, 936 relevant symptoms of the digestive, reproductive, neurological and other systems, 371 types of complications, 838,407 types of bacteria, 341 types of antibiotics, 1504 pairs of reaction rates (antibacterial spectrum) between antibiotics and bacteria, 431 pairs of drug interaction relationships and 86 pairs of antibiotic-specific population contraindicated relationships. Compared with the existing infectious disease-relevant ontologies in the field of knowledge comprehension, this ontology is more complete. Analysis of IDDAP's performance in terms of classifiers based on receiver operating characteristic (ROC) curve results (89.91%) revealed IDDAP's advantages when combined with our ontology. CONCLUSIONS AND SIGNIFICANCE: This study attempted to bridge the patient/caregiver gap by building a sophisticated application that uses artificial intelligence and machine learning computational techniques to perform data-driven decision-making at the point of primary care. The first level of decision-making is conducted by the IDDAP and provides the patient with a first-line therapy. Patients can then make a subjective judgment, and if any questions arise, should consult a physician for subsequent decisions, particularly in complicated cases or in cases in which the necessary information is not yet available in the knowledge base.

On the Discovery of Evolving Truth.

In the era of big data, information regarding the same objects can be collected from increasingly more sources. Unfortunately, there usually exist conflicts among the information coming from different sources. To tackle this challenge, truth discovery, i.e., to integrate multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. In many real world applications, however, the information may come sequentially, and as a consequence, the truth of objects as well as the reliability of sources may be dynamically evolving. Existing truth discovery methods, unfortunately, cannot handle such scenarios. To address this problem, we investigate the temporal relations among both object truths and source reliability, and propose an incremental truth discovery framework that can dynamically update object truths and source weights upon the arrival of new data. Theoretical analysis is provided to show that the proposed method is guaranteed to converge at a fast rate. The experiments on three real world applications and a set of synthetic data demonstrate the advantages of the proposed method over state-of-the-art truth discovery methods.

Performance of dynamic features in classifying scalp epileptic interictal and normal EEG.

Over 50 million people worldwide suffer from epilepsy. Recently, researchers have proposed computer-aided epilepsy diagnostic systems based on classifying scalp epileptic interictal and normal EEG. Features used in the classification can be divided into two groups: classical spectral features and dynamic features. Classical spectral features are similar to major frequency component identification that physicians use in conventional EEG reading. Because dynamic features are new compared to classical spectral features, we are interested in knowing whether they are suitable for this classification problem. To study this, we build such a system and compare the results between using classical spectral features and dynamic features. Furthermore, we study which dynamic features are more suitable, i.e., more discriminative, by ranking them using F-score. According to the result, we discuss redesigning certain dynamic features for better classification. This research is a preliminary study of using dynamic features of scalp interictal EEG for epilepsy diagnosis.