A Conformal Carbon Nanolayer Coated Fe2 O3 Cocatalyst for the Promoted Activity of Plasma-Sputtered BiVO4 Photoanode.

To simultaneously improve the hole extraction ability of the BiVO4 photoanode and accelerate the surface reaction kinetics, herein, a carbon nanolayer conformally coated Fe2 O3 (C-Fe2 O3 ) as oxygen evolution catalyst (OEC) is loaded on the H2 plasma treated nanoporous BiVO4 (BVO(H2 )) surface by a hydrothermal reaction. It is found that the H2 plasma induced vacancies in BVO remarkably increases the conductivity, and the C-Fe2 O3 enables hole extraction from the bulk to the surface as well as efficient hole injection to the electrolyte. As a result, the C-Fe2 O3 /BVO(H2 ) photoanode achieves a photocurrent density of 4.4 mA/cm2 at 1.23 V vs. reversible hydrogen electrode (RHE) and an ABPE value of 1.5 % at 0.68 V vs. RHE, which are 4.8-fold and 13-fold higher than that of BVO photoanode, respectively.

Feature extraction for phenotyping from semantic and knowledge resources.

OBJECTIVE: Phenotyping algorithms can efficiently and accurately identify patients with a specific disease phenotype and construct electronic health records (EHR)-based cohorts for subsequent clinical or genomic studies. Previous studies have introduced unsupervised EHR-based feature selection methods that yielded algorithms with high accuracy. However, those selection methods still require expert intervention to tweak the parameter settings according to the EHR data distribution for each phenotype. To further accelerate the development of phenotyping algorithms, we propose a fully automated and robust unsupervised feature selection method that leverages only publicly available medical knowledge sources, instead of EHR data. METHODS: SEmantics-Driven Feature Extraction (SEDFE) collects medical concepts from online knowledge sources as candidate features and gives them vector-form distributional semantic representations derived with neural word embedding and the Unified Medical Language System Metathesaurus. A number of features that are semantically closest and that sufficiently characterize the target phenotype are determined by a linear decomposition criterion and are selected for the final classification algorithm. RESULTS: SEDFE was compared with the EHR-based SAFE algorithm and domain experts on feature selection for the classification of five phenotypes including coronary artery disease, rheumatoid arthritis, Crohn's disease, ulcerative colitis, and pediatric pulmonary arterial hypertension using both supervised and unsupervised approaches. Algorithms yielded by SEDFE achieved comparable accuracy to those yielded by SAFE and expert-curated features. SEDFE is also robust to the input semantic vectors. CONCLUSION: SEDFE attains satisfying performance in unsupervised feature selection for EHR phenotyping. Both fully automated and EHR-independent, this method promises efficiency and accuracy in developing algorithms for high-throughput phenotyping.

Evaluating semantic similarity between Chinese biomedical terms through multiple ontologies with score normalization: An initial study.

BACKGROUND: Semantic similarity estimation significantly promotes the understanding of natural language resources and supports medical decision making. Previous studies have investigated semantic similarity and relatedness estimation between biomedical terms through resources in English, such as SNOMED-CT or UMLS. However, very limited studies focused on the Chinese language, and technology on natural language processing and text mining of medical documents in China is urgently needed. Due to the lack of a complete and publicly available biomedical ontology in China, we only have access to several modest-sized ontologies with no overlaps. Although all these ontologies do not constitute a complete coverage of biomedicine, their coverage of their respective domains is acceptable. In this paper, semantic similarity estimations between Chinese biomedical terms using these multiple non-overlapping ontologies were explored as an initial study. METHODS: Typical path-based and information content (IC)-based similarity measures were applied on these ontologies. From the analysis of the computed similarity scores, heterogeneity in the statistical distributions of scores derived from multiple ontologies was discovered. This heterogeneity hampers the comparability of scores and the overall accuracy of similarity estimation. This problem was addressed through a novel language-independent method by combining semantic similarity estimation and score normalization. A reference standard was also created in this study. RESULTS: Compared with the existing task-independent normalization methods, the newly developed method exhibited superior performance on most IC-based similarity measures. The accuracy of semantic similarity estimation was enhanced through score normalization. This enhancement resulted from the mitigation of heterogeneity in the similarity scores derived from multiple ontologies. CONCLUSION: We demonstrated the potential necessity of score normalization when estimating semantic similarity using ontology-based measures. The results of this study can also be extended to other language systems to implement semantic similarity estimation in biomedicine.

A hierarchical method to automatically encode Chinese diagnoses through semantic similarity estimation.

BACKGROUND: The accumulation of medical documents in China has rapidly increased in the past years. We focus on developing a method that automatically performs ICD-10 code assignment to Chinese diagnoses from the electronic medical records to support the medical coding process in Chinese hospitals. METHODS: We propose two encoding methods: one that directly determines the desired code (flat method), and one that hierarchically determines the most suitable code until the desired code is obtained (hierarchical method). Both methods are based on instances from the standard diagnostic library, a gold standard dataset in China. For the first time, semantic similarity estimation between Chinese words are applied in the biomedical domain with the successful implementation of knowledge-based and distributional approaches. Characteristics of the Chinese language are considered in implementing distributional semantics. We test our methods against 16,330 coding instances from our partner hospital. RESULTS: The hierarchical method outperforms the flat method in terms of accuracy and time complexity. Representing distributional semantics using Chinese characters can achieve comparable performance to the use of Chinese words. The diagnoses in the test set can be encoded automatically with micro-averaged precision of 92.57 %, recall of 89.63 %, and F-score of 91.08 %. A sharp decrease in encoding performance is observed without semantic similarity estimation. CONCLUSION: The hierarchical nature of ICD-10 codes can enhance the performance of the automated code assignment. Semantic similarity estimation is demonstrated indispensable in dealing with Chinese medical text. The proposed method can greatly reduce the workload and improve the efficiency of the code assignment process in Chinese hospitals.

A MOF-derived Bimetallic Co-catalyst for Promoting Visible-light Photocatalytic Hydrogen Evolution on NH2 -MIL-125.

In order to construct noble metal-free co-catalysts to facilitate the transport and separation of photocatalyst carriers, herein, a MOF-based derived bimetallic NiCu0.2 co-catalyst was loaded on NH2 -MIL-125(Ti). The obtained NiCu0.2 /NH2 -MIL-125 exhibited a photocatalytic activity of 161.4 µmol g-1 h-1 for hydrogen evolution, 12.6 times higher than that of the Ni/NH2 -MIL-125 and even slightly better than Pt/NH2 -MIL-125. The work expands the development pathway of cost-effective and highly active bimetallic co-catalysts for photocatalytic H2 evolution.

Construction of a ruthenium-doped CoFe-layered double hydroxide as a bifunctional electrocatalyst for overall water splitting.

In this study, ruthenium-doped CoFe-based layered double hydroxides on Ni foam (CoFe-ZLDH/Ru@NF) were fabricated via an etching-precipitation strategy. The resultant CoFe-ZLDH/Ru@NF exhibited excellent activity, showing low overpotentials of 219.8 mV and 60.9 mV to reach the current density of 10 mA cm-2 for the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), respectively. As a bifunctional electrocatalyst, it was assembled in an anion exchange membrane water electrolyser (AEMWE) unit, performing as an anode and cathode simultaneously, which only required a cell voltage of 2.33 V to accomplish the industrial level current density of 1 A cm-2 and operated steadily for over 12 h, making it promising for utilization in hydrogen production.