Structure fusion based on graph convolutional networks for semi-supervised classification

Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture construction or the salient graph structure preservation, and ignore the the complete graph structure for semi-supervised classification contribution. To mine the more complete distribution structure from multi-view data with the consideration of the specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of semi-supervised classification. SF-GCN can not only retain the special characteristic of each view data by spectral embedding, but also capture the common style of multi-view data by distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures, we can construct the optimization function of structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification. Experiments demonstrate that the performance of SF-GCN outperforms that of the state of the arts on three challenging datasets, which are Cora,Citeseer and Pubmed in citation networks

NQE: N-ary Query Embedding for Complex Query Answering over Hyper-relational Knowledge Graphs

Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts and pay less attention to n-ary facts (n>=2) containing more than two entities, which are more prevalent in the real world. Moreover, previous CQA methods can only make predictions for a few given types of queries and cannot be flexibly extended to more complex logical queries, which significantly limits their applications. To overcome these challenges, in this work, we propose a novel N-ary Query Embedding (NQE) model for CQA over hyper-relational knowledge graphs (HKGs), which include massive n-ary facts. The NQE utilizes a dual-heterogeneous Transformer encoder and fuzzy logic theory to satisfy all n-ary FOL queries, including existential quantifiers, conjunction, disjunction, and negation. We also propose a parallel processing algorithm that can train or predict arbitrary n-ary FOL queries in a single batch, regardless of the kind of each query, with good flexibility and extensibility. In addition, we generate a new CQA dataset WD50K-NFOL, including diverse n-ary FOL queries over WD50K. Experimental results on WD50K-NFOL and other standard CQA datasets show that NQE is the state-of-the-art CQA method over HKGs with good generalization capability. Our code and dataset are publicly available.Comment: Accepted by the 37th AAAI Conference on Artificial Intelligence (AAAI-2023

Application of Machine Learning in Microbiology

Microorganisms are ubiquitous and closely related to people’s daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs still significantly relies on manual labor, and n-ary relation extraction still remains at a course-grained level, which is always in a single schema and fixed arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. Experimental results demonstrate that Text2NKG outperforms the previous state-of-the-art model by nearly 20\% points in the $F_1$ scores on the fine-grained n-ary relation extraction benchmark in the hyper-relational schema. Our code and datasets are publicly available.Comment: Preprin

Association between prophylactic hydration volume and risk of contrast-induced nephropathy after emergent percutaneous coronary intervention

Background: Intravenous hydration during percutaneous coronary intervention (PCI) significantly reduces the risk of contrast-induced nephropathy (CIN), but there are no well-defined protocols regard¬ing the optimal hydration volume (HV) required to prevent CIN following emergent PCI. Therefore, this study investigates the association between the intravenous HV and CIN after emergent PCI. Methods: 711 patients were prospectively recruited who had underwent emergent PCI with hydration at routine speed and the relationship was investigated between HV or HV to weight ratio (HV/W) and the CIN risk, which was defined as a ≥ 25% or ≥ 0.5 mg/dL increase in serum creatinine levels from baseline within 48–72 h of exposure to the contrast. Results: The overall CIN incidence was 24.7%. Patients in the higher HV quartiles had elevated CIN rates. Multivariate analysis showed that higher HV/W ratios were not associated with a decreased risk (using the HV) of CIN, but they were associated with an increased risk (using the HV/W) of CIN (Q4 vs. Q1: adjusted odds ratio 1.99; 95% confidence interval 1.05–3.74; p = 0.034). A higher HV/W ratio was not significantly associated with a reduced risk of long-term death (all p > 0.05). Conclusions: The data suggests that a higher total HV is not associated with a decreased CIN risk or beneficial long-term prognoses, and that excessive HV may increase the risk of CIN after emergent PCI

MELD-XI Score Is Associated With Short-Term Adverse Events in Patients With Heart Failure With Preserved Ejection

Aim: Accumulating evidence suggests that MELD-XI score holds the ability to predict the prognosis of congestive heart failure. However, most of the evidence is based on the end-stage heart failure population; thus, we aim to explore the association between the MELD-XI score and the prognosis in heart failure with preserved ejection fraction (HFpEF).Methods: A total of 30,096 patients hospitalized for HFpEF in Fujian Provincial Hospital between January 1, 2014 and July 17, 2020 with available measures of creatinine and liver function were enrolled. The primary endpoint was 60-day in-hospital all-cause mortality. Secondary endpoints were 60-day in-hospital cardiovascular mortality and 30-day rehospitalization for heart failure.Results: A total of 222 patients died within 60 days after admission, among which 75 deaths were considered cardiogenic. And 73 patients were readmitted for heart failure within 30 days after discharge. Generally, patients with an elevated MELD-XI score tended to have more comorbidities, higher NYHA class, and higher inflammatory biomarkers levels. Meanwhile, the MELD-XI score was positively correlated with NT-pro BNP, left atrial diameter, E/e' and negatively correlated with LVEF. After adjusting for conventional risk factors, the MELD-XI score was independently associated with 60-day in-hospital all-cause mortality [hazard ratio(HR) = 1.052, 95% confidential interval (CI) 1.022–1.083, P = 0.001], 60-day in-hospital cardiovascular mortality (HR = 1.064, 95% CI 1.013–1.118, P = 0.014), and 30-day readmission for heart failure (HR = 1.061, 95% CI 1.015–1.108, P = 0.009). Furthermore, the MELD-XI score added an incremental discriminatory capacity to risk stratification models developed based on this cohort.Conclusion: The MELD-XI score was associated with short-term adverse events and provided additional discriminatory capacity to risk stratification models in patients hospitalized for HFpEF