Ion Irradiation Inducing Oxygen Vacancy-Rich NiO/NiFe2 O4 Heterostructure for Enhanced Electrocatalytic Water Splitting.

Oxygen evolution reaction (OER) is an obstacle to the electrocatalytic water splitting due to its unique four-proton-and-electron-transfer reaction process. Many methods, such as engineering heterostructure and introducing oxygen vacancy, have been used to improve the catalytic performance of electrocatalysts for OER. Herein, the above two kinds of regulation are simultaneously realized in a catalyst by using unique ion irradiation technology. A nanosheet structured NiO/NiFe2 O4 heterostructure with rich oxygen vacancies converted from nickel-iron layered double hydroxides by Ar+ ions irradiation shows significant enhancement in both OER and hydrogen evolution reaction performance. Density functional theory (DFT) calculations reveal that the construction of NiO/NiFe2 O4 can optimize the free energy of O* to OOH* process during OER reaction. The oxygen vacancy-rich NiO/NiFe2 O4 nanosheets have an overpotential of 279 mV at 10 mA cm-2 and a low Tafel slope of 42 mV dec-1 . Moreover, this NiO/NiFe2 O4 electrode shows an excellent long-term stability at 100 mA cm-2 for 450 h. The synergetic effects between NiO and NiFe2 O4 make NiO/NiFe2 O4 heterostructure have high conductivity and fast charge transfer, abundant active sites, and high catalytic reactivity, contributing to its excellent performance.

Mixture drug-count response model for the high-dimensional drug combinatory effect on myopathy.

Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The "count" indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies.

Formation of tungsten oxide nanowires by ion irradiation and vacuum annealing.

Here we reported the fabrication of tungsten oxide (WO3-x ) nanowires by Ar+ ion irradiation of WO3 thin films followed by annealing in vacuum. The nanowire length increases with increasing irradiation fluence and with decreasing ion energy. We propose that the stress-driven diffusion of the irradiation-induced W interstitial atoms is responsible for the formation of the nanowires. Comparing to the pristine film, the fabricated nanowire film shows a 106-fold enhancement in electrical conductivity, resulting from the high-density irradiation-induced vacancies on the oxygen sublattice. The nanostructure exhibits largely enhanced surface-enhanced Raman scattering effect due to the oxygen vacancy. Thus, ion irradiation provides a powerful approach for fabricating and tailoring the surface nanostructures of semiconductors.

Using machine learning algorithms to identify genes essential for cell survival.

BACKGROUND: With the explosion of data comes a proportional opportunity to identify novel knowledge with the potential for application in targeted therapies. In spite of this huge amounts of data, the solutions to treating complex disease is elusive. One reason being that these diseases are driven by a network of genes that need to be targeted in order to understand and treat them effectively. Part of the solution lies in mining and integrating information from various disciplines. Here we propose a machine learning method to mining through publicly available literature on RNA interference with the goal of identifying genes essential for cell survival. RESULTS: A total of 32,164 RNA interference abstracts were identified from 10.5 million pubmed abstracts (2001 - 2015). These abstracts spanned over 1467 cancer cell lines and 4373 genes representing a total of 25,891 cell gene associations. Among the 1467 cell lines 88% of them had at least 1 or up to 25 genes studied in a given cell line. Among the 4373 genes 96% of them were studied in at least 1 or up to 25 different cell lines. CONCLUSIONS: Identifying genes that are crucial for cell survival can be a critical piece of information especially in treating complex diseases, such as cancer. The efficacy of a therapeutic intervention is multifactorial in nature and in many cases the source of therapeutic disruption could be from an unsuspected source. Machine learning algorithms helps to narrow down the search and provides information about essential genes in different cancer types. It also provides the building blocks to generate a network of interconnected genes and processes. The information thus gained can be used to generate hypothesis which can be experimentally validated to improve our understanding of what triggers and maintains the growth of cancerous cells.

Recent Progress in the Application of Ion Beam Technology in the Modification and Fabrication of Nanostructured Energy Materials.

The development of green, renewable energy conversion and storage systems is an urgent task to address the energy crisis and environmental issues in the future. To achieve high performance, stable, and safe operation of energy conversion and storage systems, energy materials need to be modified and fabricated through rationalization. Among various modification and fabrication strategies, ion beam technology has been widely used to introduce various defects/dopants into energy materials and fabricate various nanostructures, where the structure, composition, and property of prefabricated materials can be further accurately tailored to achieve better performance. In this paper, we review the recent progress in the application of ion beam technology in material modification and fabrication, focusing on nanostructured energy materials for energy conversion and storage including photo- (electro-) water splitting, batteries (solar cells, fuel cells, and metal-ion batteries), supercapacitors, thermoelectrics, and hydrogen storage. This review first provides a brief basic overview of ion beam technology and describes the classification and technological advantages of ion beam technology in the modification and fabrication of materials. Then, modification of energy materials by ion beams is reviewed mainly concerning doping and defect introduction. Fabrication of energy materials is also discussed mainly in terms of heterojunctions, nanoparticles, nanocavities, and other nanostructures. In particular, we emphasize the advantages of ion beam technology in improving the performance of energy materials. Finally, we point out our understanding of challenges and future perspectives in applying ion beam technology for the modification and fabrication of energy materials.

An integrated pharmacokinetics ontology and corpus for text mining.

BACKGROUND: Drug pharmacokinetics parameters, drug interaction parameters, and pharmacogenetics data have been unevenly collected in different databases and published extensively in the literature. Without appropriate pharmacokinetics ontology and a well annotated pharmacokinetics corpus, it will be difficult to develop text mining tools for pharmacokinetics data collection from the literature and pharmacokinetics data integration from multiple databases. DESCRIPTION: A comprehensive pharmacokinetics ontology was constructed. It can annotate all aspects of in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. It covers all drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK-corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacogenetic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK-corpus was demonstrated by a drug interaction extraction text mining analysis. CONCLUSIONS: The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK-corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug interactions.

A modulator based regulatory network for ERα signaling pathway.

BACKGROUND: Estrogens control multiple functions of hormone-responsive breast cancer cells. They regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. ERα requires distinct co-regulator or modulators for efficient transcriptional regulation, and they form a regulatory network. Knowing this regulatory network will enable systematic study of the effect of ERα on breast cancer. METHODS: To investigate the regulatory network of ERα and discover novel modulators of ERα functions, we proposed an analytical method based on a linear regression model to identify translational modulators and their network relationships. In the network analysis, a group of specific modulator and target genes were selected according to the functionality of modulator and the ERα binding. Network formed from targets genes with ERα binding was called ERα genomic regulatory network; while network formed from targets genes without ERα binding was called ERα non-genomic regulatory network. Considering the active or repressive function of ERα, active or repressive function of a modulator, and agonist or antagonist effect of a modulator on ERα, the ERα/modulator/target relationships were categorized into 27 classes. RESULTS: Using the gene expression data and ERα Chip-seq data from the MCF-7 cell line, the ERα genomic/non-genomic regulatory networks were built by merging ERα/ modulator/target triplets (TF, M, T), where TF refers to the ERα, M refers to the modulator, and T refers to the target. Comparing these two networks, ERα non-genomic network has lower FDR than the genomic network. In order to validate these two networks, the same network analysis was performed in the gene expression data from the ZR-75.1 cell. The network overlap analysis between two cancer cells showed 1% overlap for the ERα genomic regulatory network, but 4% overlap for the non-genomic regulatory network. CONCLUSIONS: We proposed a novel approach to infer the ERα/modulator/target relationships, and construct the genomic/non-genomic regulatory networks in two cancer cells. We found that the non-genomic regulatory network is more reliable than the genomic regulatory network.

Translational drug-interaction corpus.

The discovery of drug-drug interactions (DDIs) that have a translational impact among in vitro pharmacokinetics (PK), in vivo PK and clinical outcomes depends largely on the quality of the annotated corpus available for text mining. We have developed a new DDI corpus based on an annotation scheme that builds upon and extends previous ones, where an abstract is fragmented and each fragment is then annotated along eight dimensions, namely, focus, polarity, certainty, evidence, directionality, study type, interaction type and mechanism. The guideline for defining these dimensions has undergone refinement during the annotation process. Our DDI corpus comprises 900 positive DDI abstracts and 750 that are not directly relevant to DDI. The abstracts in corpus are separated into eight categories of DDI or non-DDI evidence: DDI with pharmacokinetic (PK) mechanism, in vivo DDI PK, DDI clinical, drug-nutrition interaction, single drug, not drug related, in vitro pharmacodynamic (PD) and case report. Seven annotators, three annotators with drug-interaction research experience and four annotators with less drug-interaction research experience independently annotated the DDI corpus, where two researchers independently annotated each abstract. After two rounds of annotations with additional training in between, agreement improved from (0.79, 0.96, 0.86, 0.70, 0.91, 0.65, 0.78, 0.90) to (0.93, 0.99, 0.96, 0.94, 0.95, 0.93, 0.96, 0.97) for focus, certainty, evidence, study type, interaction type, mechanisms, polarity and direction, respectively. The novice-level annotators improved from 0.83 to 0.96, while the expert-level annotators stayed in high performance with some improvement, from 0.90 to 0.96. In summary, we achieved 96% agreement among each pair of annotators with regard to the eight dimensions. The annotated corpus is now available to the community for inclusion in their text-mining pipelines. Database URL https://github.com/zha204/DDI-Corpus-Database/tree/master/DDI%20corpus.

PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature.

BACKGROUND: Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks. RESULTS: Two annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the "Biomedical & Procedure" dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for "The use of NLP". The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions. CONCLUSIONS: The corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications.

Translational Knowledge Discovery Between Drug Interactions and Pharmacogenetics.

Clinical translation of drug-drug interaction (DDI) studies is limited, and knowledge gaps across different types of DDI evidence make it difficult to consolidate and link them to clinical consequences. Consequently, we developed information retrieval (IR) models to retrieve DDI and drug-gene interaction (DGI) evidence from 25 million PubMed abstracts and distinguish DDI evidence into in vitro pharmacokinetic (PK), clinical PK, and clinical pharmacodynamic (PD) studies for US Food and Drug Administration (FDA) approved and withdrawn drugs. Additionally, information extraction models were developed to extract DDI-pairs and DGI-pairs from the IR-retrieved abstracts. An overlapping analysis identified 986 unique DDI-pairs between all 3 types of evidence. Another 2,157 and 13,012 DDI-pairs and 3,173 DGI-pairs were identified from known clinical PK/PD DDI, clinical PD DDI, and DGI evidence, respectively. By integrating DDI and DGI evidence, we discovered 119 and 18 new pharmacogenetic hypotheses associated with CYP3A and CYP2D6, respectively. Some of these DGI evidence can also aid us in understanding DDI mechanisms.

DrugMetab: An Integrated Machine Learning and Lexicon Mapping Named Entity Recognition Method for Drug Metabolite.

Drug metabolites (DMs) are critical in pharmacology research areas, such as drug metabolism pathways and drug-drug interactions. However, there is no terminology dictionary containing comprehensive drug metabolite names, and there is no named entity recognition (NER) algorithm focusing on drug metabolite identification. In this article, we developed a novel NER system, DrugMetab, to identify DMs from the PubMed abstracts. DrugMetab utilizes the features characterized from the Part-of-Speech, drug index, and pre/suffix, and determines DMs within context. To evaluate the performance, a gold-standard corpus was manually constructed. In this task, DrugMetab with sequential minimal optimization (SMO) classifier achieves 0.89 precision, 0.77 recall, and 0.83 F-measure in the internal testing set; and 0.86 precision, 0.85 recall, and 0.86 F-measure in the external validation set. We further compared the performance between DrugMetab and whatizitChemical, which was designed for identifying small molecules or chemical entities. DrugMetab outperformed whatizitChemical, which had a lower recall rate of 0.65.

Three-Component Mixture Model-Based Adverse Drug Event Signal Detection for the Adverse Event Reporting System.

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is an important source for detecting adverse drug event (ADE) signals. In this article, we propose a three-component mixture model (3CMM) for FAERS signal detection. In 3CMM, a drug-ADE pair is assumed to have either a zero relative risk (RR), or a background RR (mean RR = 1), or an increased RR (mean RR >1). By clearly defining the second component (mean RR = 1) as the null distribution, 3CMM estimates local false discovery rates (FDRs) for ADE signals under the empirical Bayes framework. Compared with existing approaches, the local FDR's top signals have noninferior or better sensitivities to detect true signals in both FAERS analysis and simulation studies. Additionally, we identify that the top signals of different approaches have different patterns, and they are complementary to each other.

Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research.

Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.

[A survey of present situation of general intensive care unit in second grade hospitals and construction of intensive care network in Guangdong province].

OBJECTIVE: To investigate the present situation of general intensive care unit (ICU) in second grade hospitals, and to establish intensive care network for the Department of Public Health of Guangdong province in Guangdong province. METHODS: Data from ICU of 26 hospitals in Guangdong were collected through questionnaire concerning different aspects of critical care medicine. RESULTS: (1) ICU size was (10.12+/-3.82) beds per unit, ratios of doctors to beds and nurses to beds were 0.73+/-0.25 and 1.80+/-0.57 respectively, and proportions of closed model or semi-closed model of ICU management were 69.2% and 26.9% respectively. (2) Area occupied by per bed was (17.57+/-7.58) m2, ratio of basins with infrared control facet to beds was 0.47+/-0.33, proportions of ICU equipped with room equipped with positive or negative air pressure, laminar flow, or with room for preparing nutrition support were 15.4%, 30.8%, and 23.1% respectively. (3) All the ICU were capable of institution and management of artificial airway, mechanical ventilation, placement of deep vein line, cardioversion and defibrillation, parenteral nutrition, and sedation. Ninety-six point two percent of the ICU could accomplish trachea intubation independently. Fifty-three point eight percent of the ICU could perform hemodynamic monitoring. Continuous blood purification could be done in 73.1 % of the ICU. (4) Ninety-six point two percent of the ICU were equipped with continuous bedside multifunctional electrocardiogram monitor and ratio of the monitors to beds was 0.89+/-0.29. Ratios of resuscitation air bags to beds and ventilators to beds were 0.71+/-0.34 and 0.71+/-0.24 respectively. Portable ventilator was equipped in 34.6 % of the ICU. Forty percent of the ICU could not perform non-invasive ventilation, 65.4 % of the ICU were equipped with fiberoptic bronchoscope, blood gas analysis could be done during 24 hours round in 92.3 % of the ICU. (5) Twenty-six ICU investigated were found to be distributed over the district of Zhujiang delta, and east, north and west regions of Guangdong, forming the base of intensive care network in Guangdong province. CONCLUSION: Most of the general ICU in second grade hospitals in Guangdong province have fulfilled the main requirement for ICU in accordance with the guidelines for construction of ICU in Guangdong province and of guidelines for construction and management of ICU in China. The average level of the ICU is close to level II. It is possible for the intensive care network to integrate, the present resources effectively and then enhance the level of treatment of critical illness in the said district.

Enhanced PEC performance of nanoporous Si photoelectrodes by covering HfO2 and TiO2 passivation layers.

Nanostructured Si as the high efficiency photoelectrode material is hard to keep stable in aqueous for water splitting. Capping a passivation layer on the surface of Si is an effective way of protecting from oxidation. However, it is still not clear in the different mechanisms and effects between insulating oxide materials and oxide semiconductor materials as passivation layers. Here, we compare the passivation effects, the photoelectrochemical (PEC) properties, and the corresponding mechanisms between the HfO2/nanoporous-Si and the TiO2/nanoporous-Si by I-V curves, Motte-schottky (MS) curves, and electrochemical impedance spectroscopy (EIS). Although the saturated photocurrent densities of the TiO2/nanoporous Si are lower than that of the HfO2/nanoporous Si, the former is more stable than the later.

A General Method for Large-Scale Fabrication of Semiconducting Oxides with High SERS Sensitivity.

Surface-enhanced Raman spectroscopy (SERS) is a versatile and powerful spectroscopic technique for substance analysis and detection. So far, the highest detection sensitivities have been realized on noble nanostructure substrates, which, however, are costly, unstable, and non-biocompatible. While semiconductor substrates could in principle be used, existing realizations have either resulted in substrates with low sensitivities or used methods that have poor technical control. Here we report a general and versatile method, based on ion irradiation and vacuum annealing, for fabricating large-scale reduced semiconducting oxide SERS substrates with high sensitivities. The SERS enhancement mainly stems from oxygen vacancy-associated electronic states created by the ion irradiation of sample; these states enhance the charge-transfer (CT) mechanism between the oxide substrate and the adsorbed molecules and thus significantly magnify SERS signals. The improved carrier mobility by vacuum annealing and the introduction of impurity energy levels and nanostructures enhances further the CT efficiency. A detection limit as low as 5 × 10-8 M was achieved; this is the highest sensitivity among the reported semiconductors, and it even compares to noble metals without the aid of "hot spots". The method is general-we demonstrate it on WO3, ZnO, and TiO2 substrates using Ar+ and N+ ion beam irradiation-and broadly applicable to produce noble-metal-free SERS substrates with high sensitivities.

Erratum to: Extracting drug-enzyme relation from literature as evidence for drug drug interaction.

This corrects the article DOI: 10.1186/s13326-016-0052-6.

Extracting drug-enzyme relation from literature as evidence for drug drug interaction.

BACKGROUND: Information about drug-drug interactions (DDIs) is crucial for computational applications such as pharmacovigilance and drug repurposing. However, existing sources of DDIs have the problems of low coverage, low accuracy and low agreement. One common type of DDIs is related to the mechanism of drug metabolism: a DDI relation may be caused by different interactions (e.g., substrate, inhibit) between drugs and enzymes in the drug metabolism process. Thus, information from drug enzyme interactions (DEIs) serves as important supportive evidence for DDIs. Further, potential DDIs present implicitly could be detected by inference and reasoning based on DEIs. METHODS: In this article, we propose a hybrid approach to combining machine learning algorithm with trigger words and syntactic patterns, for DEI relation extraction from biomedical literature. The extracted DEI relations are used for reasoning to infer potential DDI relations, based on a defined drug-enzyme ontology incorporating biological knowledge. RESULTS: Evaluation results demonstrate that the performance of DEI relation extraction is promising, with an F-measure of 84.97% on the in vivo dataset and 65.58% on the in vitro dataset. Further, the inferred DDIs achieved a precision of 83.19% on the in vivo dataset and 70.94% on the in vitro dataset, respectively. A further examination showed that the overlaps between our inferred DDIs and those present in DrugBank were 42.02% on the in vivo dataset and 19.23 % on the in vitro dataset, respectively. CONCLUSIONS: This paper proposed an effective approach to extract DEI relations from biomedical literature. Potential DDIs not present in existing knowledge bases were then inferred based on the extracted DEIs, demonstrating the capability of the proposed approach to detect DDIs with scientific evidence for pharmacovigilance and drug repurposing applications.

Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature.

BACKGROUND: Information about drug-drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs. RESULTS: When evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively. CONCLUSIONS: We presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure.