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miR-29a plays a vital role in AS, but the relationship between the miR-29a-targeted PI3K signaling pathway and AS remains unclear. Therefore, this study was carried out.Gene expression profiles from the GEO database containing AS samples were analyzed. ApoEDEGs were identified from GSE137578, GSE132651, GSE113969, GSE43292, and GSE97210 datasets. It was found that there were targeted binding sites between miR-29a and PIK3CA. Besides, GO and KEGG analysis demonstrated that autophagy was an enriched pathway in AS. Later, PPI network was depicted, and hub genes were then determined. The results revealed that miR-29a suppressed the areas of plaques and lesional macrophages, but had no impact on VSMCs. TEM results showed the organelles pyknosis of lesional macrophages damaged morphological changes. Furthermore, miR-29a amplified the M2-like macrophages but suppressed the polarization of M1-like macrophages in atherosclerotic plaques. According to mouse and RAW 264.7 cell experiments, miR-29a significantly inhibited the protein expressions of PI3K, p-PI3K, p-AKT, and p-mTOR, which were consistent with the increased expressions of autophagy-related proteins, Beclin 1 and LC3II. However, the miR-29a suppression exhibited the contrary results.MiR-29a elevation induces the increase of autophagy by down-regulating the PI3K/AKT/mTOR pathway in the progression of AS, indicating that miR-29a is a novel therapeutic strategy for AS.

China’s transformation into the world’s largest energy consumer constitutes one of the most profound changes to the global energy system in recent years. In providing the low-carbon technology sector with financial assistance and technical solutions aimed at developing clean energy across the world, China has proactively contributed to global energy governance. China’s emergence as a global leader in renewable energy development and emissions reduction, therefore, carries positive implications for its future role in global renewable energy governance. In seeking to understand the structure and trends of China’s domestic renewable energy governance, this study analyses what role China can and will play in the global sphere. Specifically, by examining how its domestic renewable energy context shapes its global approach in this regard, the study analyses two aspects of China’s engagement in global renewable energy governance: the institutional aspect and the market aspect. The study concludes by making recommendations as to how China may play a more positive and active role in global renewable energy governance.

Several international norms oblige governments of countries planning to build nuclear installations to proactively consult potentially affected countries in a timely manner. These norms arise from customary international law, the Convention on Nuclear Safety (CNS), the guidelines of the International Atomic Energy Agency (IAEA) and regional treaties and international environmental law. This study has examined 15 examples to assess (i) the extent to which such consultation has taken place and (ii) the effectiveness of measures taken by potentially affected countries in those cases where prior consultation had not taken place. Examples range in vintage from the 1960s to the present day and include both newbuild and newcomer countries. In only 3 of these 15 cases, prior consultation had taken place and all the involved nations had good bilateral relations and/or common interests in the project. Nevertheless, disputes arose later in two of these cases. In the other examples, the degree of success of the potentially affected state in changing the behaviour of the newbuild or newcomer state depended on a number of factors; for example, the state of bilateral relations, the availability of a regional organization to resolve the dispute, the validity of the arguments of both parties and the goodwill of the newbuild or newcomer state. Whilst the CNS and the actions of the IAEA have raised nuclear safety standards substantially over recent decades, the unwillingness of most nations to adhere to the obligation to consult would seem to be a significant weakness.

Background The COVID-19 pandemic has disrupted healthcare, including immunization practice and well child visit attendance. Maintaining vaccination coverage is important to prevent disease outbreaks and morbidity. We assessed the impact of the COVID-19 pandemic on pediatric and adolescent vaccination administration and well child visit attendance in the United States. Methods This cross-sectional study used IBM MarketScan Commercial Database (IMC) with Early View (healthcare claims database) and TriNetX Dataworks Global Network (electronic medical records database) from January 2018–March 2021. Individuals ≤ 18 years of age who were enrolled during the analysis month of interest (IMC with Early View) or had ≥ 1 health encounter at a participating institution (TriNetX Dataworks) were included. We calculated the monthly percent difference between well child visit attendance and vaccine administration rates for 10 recommended pediatric/adolescent vaccines in 2020 and 2021 compared with 2018–2019. Data were stratified by the age groups 0–2 years, 4–6 years, and 9–16 years. Results In IMC with Early View, the average monthly enrollment for children 0–18 years of age was 5.2 million. In TriNetX Dataworks, 12.2 million eligible individuals were included. Well child visits and vaccinations reached the lowest point in April 2020 compared with 2018–2019. Well child visit attendance and vaccine administration rates were inversely related to age, with initial reductions highest for adolescents and lowest for ages 0–2 years. Rates rebounded in June and September 2020 and stabilized to pre-pandemic levels in Fall 2020. Rates dropped below baseline in early 2021 for groups 0–2 years and 4–6 years. Conclusions We found substantial disruptions in well child visit attendance and vaccination administration for children and adolescents during the COVID-19 pandemic in 2020 and early 2021. Continued efforts are needed to monitor recovery and catch up to avoid outbreaks and morbidity associated with vaccine-preventable diseases.

Endothelial-to-mesenchymal transition (EndMT) is involved in cardiac fibrosis induced by angiotensin II (Ang II). A disintegrin and metalloproteinase 8 (ADAM8), a member of ADAMs family, participates in cell adhesion, proteolysis and various signaling. However, its effects on the development of cardiac fibrosis remain completely unknown. This study aimed to reveal whether ADAM8 aggravates cardiac fibrosis induced by Ang II in vivo and in vitro. The C57BL/6J mice or cardiac endothelial cells were subjected to Ang II infusion to induce fibrosis. The results showed that systolic blood pressure and diastolic blood pressure were significantly increased under Ang II infusion, and ADAM8 was up-regulated. ADAM8 inhibition attenuated Ang II-induced cardiac dysfunction. ADAM8 knockdown suppressed Ang II-induced cardiac fibrosis as evidenced by the down-regulation of CTGF, collagen I, and collagen III. In addition, the endothelial marker (VE-cadherin) was decreased, whilst mesenchymal markers (α-SMA and FSP1) were increased following Ang II infusion. However, ADAM8 repression inhibited Ang II-induced EndMT. Moreover, ADAM8 silencing repressed the activation of TGF-β1/Smad2/Smad3 pathways. Consistent with the results in vivo, we also found the inhibitory effects of ADAM8 inhibition on EndMT in vitro. All data suggest that ADAM8 promotes Ang II-induced cardiac fibrosis and EndMT via activating TGF-β1/Smad2/Smad3 pathways.

ObjectivesRoutine human papillomavirus (HPV) vaccination has been recommended in the United States since 2006 but rates remain suboptimal. State-based studies suggest that initiation in late childhood at ages 9 to 10 years compared with the recommended early adolescent ages of 11 to 12 years improves series completion. No study with national scope has explored the early initiation-HPV series completion relationship. This study addresses this knowledge gap and explores whether early initiation might improve series completion by increasing time to target completion age (time pathway) or by moving initiation to an earlier developmental stage (development pathway).MethodsUsing data from the National Immunization Survey-Teen 2017-2020, a retrospective cohort of 19 575 15 to 17 year olds who initiated HPV vaccination between ages 9 and 12 years was assembled. Time pathway endpoints were series completion by ages 13 and 15 years. The development pathway endpoint was completion within 3 years of initiation.ResultsEarly initiators were more likely to complete by ages 13 (74.0% vs 31.1%, P < .001) and 15 (91.7% vs 82.7%, P < .001) years but less likely to complete within 3 years (82.3% vs 84.9%, P = .007). The association of early initiation to completion was maintained in multivariable analyses for time pathway endpoints (age 13 years adjusted odds ratios [AOR] = 6.16; 95% confidence interval [CI], 5.45–6.96, age 15 years = AOR 2.56; 95% CI, 2.14–3.14) but not the development pathway endpoint (AOR = 0.93; 95% CI, 0.80–1.07).ConclusionsMoving routine HPV vaccination to ages 9 to 10 may improve vaccination coverage rates in early and mid-adolescence. Providers should be vigilant to patient interactions after HPV series initiation to optimize public health benefits of vaccination.

Aim: To study the effect of Rhodiola rosea injection on cardiac function and the reninangiotensin-aldosterone system (RASS) in rats with chronic heart failure. background: Rhodiola rosea injection, a traditional Chinese medication for relieving blood stasis and improving blood circulation, is an excellent therapeutic for treating coronary heart disease—angina pectoris. Rhodiola rosea injection's major component, salidroside, protects the cardiovascular system. But there isn't much first-hand evidence about how injectable Rhodiola rosea affects heart failure. Background: Rhodiola rosea injection, a traditional Chinese medication for relieving blood stasis and improving blood circulation, is an excellent therapeutic for treating coronary heart diseaseangina pectoris. Rhodiola rosea injection's major component, salidroside, protects the cardiovascular system. But there isn't much first-hand evidence about how injectable Rhodiola rosea affects heart failure. objective: In this study, a rat model of heart failure was established, and the effect of Rhodiola rosea injection on myocardial cell morphology, cardiac function, and ventricular remodelling in rats with heart failure was investigated. The influence and significance of miRNA210-3p expression provide basic research data support for Rhodiola rosea injection in the treatment of heart failure and provide a strong basis for clinical off-label drug use. Objectives: In this study, a rat model of heart failure was established, and the effect of Rhodiola rosea injection on myocardial cell morphology, cardiac function, and ventricular remodelling in rats with heart failure was investigated. method: 66 SD male rats were selected; 10 were randomly selected as a blank control group, and the remaining 56 rats were injected intraperitoneally with doxorubicin (4 μg/g), and cardiac function was measured in all rats after 6 weeks, with LVEF 60%. A successful heart failure model was established. The model rats were randomly divided into four groups: model control group, low-dose group, medium-dose group, and high-dose group of Rhodiola rosea injection, with 14 rats in each group. The 2mL/kg of Rhodiola rosea injection was injected into the tail vein once a day for 2 consecutive weeks. The blank group and the control group were given the same amount of normal saline every day. After 2 weeks, the echocardiographic index, RASS-related index, and serum BNP level were measured in all rats, and the morphology of myocardial tissue was observed by hematoxylin-eosin staining. The peripheral blood was collected to extract plasma miRNA423-5p, miRNA499-5p and miRNA210-3p. Compared with healthy control rats, the effect and significance of Rhodiola rosea injection on its expression were investigated. Methods: 66 SD male rats were selected; 10 were randomly selected as a blank control group, and 56 were treated intraperitoneally with doxorubicin (4 g/g). After 6 weeks, all animals had LVEF 60%. Established a heart failure model. Each group had 14 rats: model control, low-dose, mediumdose, and high-dose Rhodiola rosea injection. The 2mL/kg of Rhodiola rosea injection was injected into the tail vein once a day for 2 weeks. Both the blank and control groups received normal daily saline. After 2 weeks, the echocardiographic index, RASS-related index, and serum BNP level were assessed in all rats, and myocardial tissue morphology was observed. MiRNA423-5p, miRNA499-5p, and miRNA210-3p were extracted from peripheral blood. Rhodiola rosea injection on its expression was compared to healthy control rats. Results: 6 mL/kg Rhodiola rosea injection lowered LVEDV and LVESV while increasing LVEF and LVFS. Injections of 6 mL/kg Rhodiola rosea reduce plasma levels of miR-210-3p, miR-423- 5p, miRNA-499, and BNP in heart failure model rats. The 6 mL/kg Rhodiola rosea injection can restore the RASS indexes of heart failure rats to the level of the normal group. Conclusion: The present study offers preliminary evidence supporting the use of Rhodiola rosea injection in the treatment of heart failure and offers a solid foundation for clinical off-label medication use. other: N/A

Purpose In this article, we study how a Chinese real estate broker - Lianjia successfully transformed itself into Beike - China’s leading digital platform for housing transactions and services. We explain the motivation behind this platform transformation, how it turned out, and what are the lessons learned for other companies contemplating a platform transformation. Beike’s lessons are significant as they not only can help the companies achieve growth via platform transformation but also create social value by contributing to higher service quality in traditional service industries. Design/methodology/approach We draw upon comprehensive archival research into Beike, and our many years of ongoing research on platform strategy and business growth strategy. Findings This article provides important lessons for companies in traditional service industries on how to expand growth via digital platforms. We summarize four key lessons learned: 1) data is central to success in platform transformation; 2) industry knowledge and experience play an important role; 3) the right platform governance is critical in value creation; 4) harness the double powers of platform and digital transformation. Research limitations/implications More research on digital platforms and platform transformation in traditional service industries is needed to delve into the underlying factors and delineate the boundary conditions for specific details in this strategy and implementation. Practical implications This article is useful to business executives, academics, management consultants, and entrepreneurs interested in learning more about how to use digital platforms to achieve business growth and create economic and social value. In particular, Beike’s case offers inspiration and valuable lessons to companies in traditional service industries and helps them consider the factors that are important in the process of platform transformation. Social Implications This article on Beike provides an innovative solution to business leaders in traditional service industries grappling with a lack of professional standards and trust to use digital platforms to elevate service quality and create social value. Originality/value This article is unique and add value because Beike is a pioneer of using the digital platform to achieve growth and transform traditional service industries. Our study shows that platform transformation not only can help a company in a traditional industry achieve impressive growth but at the same time can create enormous social value by elevating the service quality of the whole industry.

This study uses the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BBC) models of Data Envelopment Analysis (DEA) to evaluate the relative eco-efficiency and operating efficiency of the ten petrochemical enterprises. A random sample of ten petrochemical enterprises were selected from the Northeastern area of China. The data collected from the ten petrochemical enterprises were run on the DEA models and the evaluated results were input for the difference analysis for the scale efficiency and technical efficiency. Then the estimates the petrochemical enterprise's operating efficiency and ecological efficiency along with the influencing factors were run by regression analysis in order to verify the evaluation model and the rationality of influencing factors. After the projection analysis of the DEA, the firms that did not reach the values of effectiveness were identified and provided the suggestions for the targeted improvement values of eco-efficiency. Thus, this application procedure can be viewed as an example of the application development of the new DEA model and provides the reference for related industry making sustainable development strategy.

OBJECTIVE: The COVID-19 pandemic has led to significant reductions in the administration of routinely recommended vaccines among adolescents in the US including tetanus, diphtheria, and acellular pertussis (Tdap); meningococcal (ACWY); and human papillomavirus (HPV) vaccines. The extent to which these deficits could persist in 2021 and beyond is unclear. To address this knowledge gap, this study estimated the cumulative deficits of routine vaccine doses among US adolescents during the COVID-19 pandemic and estimated the time and effort needed to recover from those deficits. METHODS: Monthly reductions in Tdap, meningococcal, and HPV doses administered to US adolescents during the COVID-19 pandemic were quantified using MarketScan Commercial Claims and Encounters data. The time and effort required to reverse the vaccination deficit under various catch-up scenarios were estimated. RESULTS: Annual doses administered of Tdap, meningococcus, and HPV vaccines decreased by 21.2%, 20.8%, and 24.0%, respectively, in 2020 compared to 2019. For 2021, the reduction in doses administered is projected to be 6%-21% compared to 2019 under different scenarios. The projected deficit of missed doses is expected to be cleared between winter 2023 and fall 2031. CONCLUSIONS: Administration rates of routine vaccines decreased significantly among US adolescents during COVID-19. Reversing these deficits to mitigate long-term health and economic consequences will require a sustained increase in vaccination rates over multiple years.

Naa50 is the catalytic subunit of N-terminal acetyltransferase complex E, which plays an important role in regulating plant development, endoplasmic reticulum stress and immune responses in Arabidopsis. In this study, the complete genomic sequence (but not the coding sequence) of Naa50 rescued the phenotype of Naa50 deletion mutants. Naa50 expression was noted in whole roots except for central root cap cells. The deletion of intron 1 resulted in a loss of Naa50 expression in the root meristem zone and in the epidermis, cortex and endodermis of the elongation zone and mature zone, while the deletion of intron 2 decreased Naa50 expression in the epidermis, cortex and endodermis of the root elongation zone and mature zone. The native Naa50 promoter together with introns 1 and 2 promotes the expression of Naa50 in sepal vascular bundles, filaments, pollen and stigmas; however, neither intron has positive effect on Naa50 expression in mature rosette leaves. The results of this study show that introns 1 and 2 in the Naa50 gene function as enhancers to promote the tissue-specific expression of Naa50.

The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a unified model to integrate disparate real-world data (RWD) sources. An integral part of the OMOP CDM is the Standardized Vocabularies (henceforth referred to as the OMOP vocabulary), which enables organization and standardization of medical concepts across various clinical domains of the OMOP CDM. For concepts with the same meaning from different source vocabularies, one is designated as the standard concept, while the others are specified as non-standard or source concepts and mapped to the standard one. However, due to the heterogeneity of source vocabularies, there may exist mapping issues such as erroneous mappings and missing mappings in the OMOP vocabulary, which could affect the results of downstream analyses with RWD. In this paper, we focus on quality assurance of vaccine concept mappings in the OMOP vocabulary, which is necessary to accurately harness the power of RWD on vaccines. We introduce a semi-automated lexical approach to audit vaccine mappings in the OMOP vocabulary. We generated two types of vaccine-pairs: mapped and unmapped, where mapped vaccine-pairs are pairs of vaccine concepts with a "Maps to" relationship, while unmapped vaccine-pairs are those without a "Maps to" relationship. We represented each vaccine concept name as a set of words, and derived term-difference pairs (i.e., name differences) for mapped and unmapped vaccine-pairs. If the same term-difference pair can be obtained by both mapped and unmapped vaccine-pairs, then this is considered as a potential mapping inconsistency. Applying this approach to the vaccine mappings in OMOP, a total of 2087 potentially mapping inconsistencies were obtained. A randomly selected 200 samples were evaluated by domain experts to identify, validate, and categorize the inconsistencies. Experts identified 95 cases revealing valid mapping issues. The remaining 105 cases were found to be invalid due to the external and/or contextual information used in the mappings that were not reflected in the concept names of vaccines. This indicates that our semi-automated approach shows promise in identifying mapping inconsistencies among vaccine concepts in the OMOP vocabulary.

PurposeGiven the digital transformation of service businesses by providing online food services and the influence of online reviews on consumers’ purchasing decisions, this study examines how service recovery attributes in different stages influence relationship marketing strategies, i.e., relationship quality and customer loyalty after service failure. This study is built upon a revised service recovery cycle model by accounting for three stages and their corresponding attributes; whereon a conceptual stage model of service recovery is proposed. This conceptual stage model incorporates stages of service recovery, their respective attributes, and how they influence relationship marketing strategies.Design/methodology/approachAn online marketing company was employed for data collection in 2019, which resulted in 301 valid responses. A Structural Equation Model (SEM) was conducted with all the data to test the relationships between the constructs. The individual measurement model was tested using the Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). A structural model was estimated using AMOS to test all the hypotheses.FindingsThe findings demonstrate that the attributes (i.e., response speed, compensation) paired with the first two stages of service recovery can significantly influence consumer loyalty in a positive state. The findings also manifest the intermediary role that relationship quality has played in the association of service recovery and consumer loyalty, which implies that the food delivery businesses could attain a more comprehended relationship quality with consumers through active and timely compensatory service recovery consumer loyalty to the food businesses.Originality/valueThis study examines how these different stages of the service recovery cycle influence the decision-making of relationship marketing strategies (i.e., relationship quality, customer loyalty) on the prerequisite of service failure. This study aspires to expand the service recovery research by objectifying a conceptual stage model of service recovery, incorporating stages’ recovery attributes and how these recovery attributes reciprocally influence relationship quality and customer loyalty.

The study of histopathological phenotypes is vital for cancer research and medicine as it links molecular mechanisms to disease prognosis. It typically involves integration of heterogenous histopathological features in whole-slide images (WSI) to objectively characterize a histopathological phenotype. However, the large-scale implementation of phenotype characterization has been hindered by the fragmentation of histopathological features, resulting from the lack of a standardized format and a controlled vocabulary for structured and unambiguous representation of semantics in WSIs. To fill this gap, we propose the Histopathology Markup Language (HistoML), a representation language along with a controlled vocabulary (Histopathology Ontology) based on Semantic Web technologies. Multiscale features within a WSI, from single-cell features to mesoscopic features, could be represented using HistoML which is a crucial step towards the goal of making WSIs findable, accessible, interoperable and reusable (FAIR). We pilot HistoML in representing WSIs of kidney cancer as well as thyroid carcinoma and exemplify the uses of HistoML representations in semantic queries to demonstrate the potential of HistoML-powered applications for phenotype characterization.

Radiofrequency catheter ablation (RFCA) is widely used to treat atrial fibrillation (AF), but its effect on left atrial (LA) remodeling in patients with AF is not completely clarified. Few studies have reported the changes in structure and function of the left atrium in patients with different types of AF after RFCA. To analyze the effect of RFCA on the LA structure and function in patients with nonvalvular paroxysmal AF, persistent AF and long-standing persistent AF (LSPAF). RFCA was performed in 180 patients with paroxysmal AF, persistent AF and LSPAF. The changes of LA structure and function in echocardiogram and speckle-tracking echocardiography findings were compared before the procedure, and at 1, 2, 3, 4 weeks, and 2, 3, 6, and 9–12 months after the procedure. There were 60 patients in the paroxysmal AF group, 60 in the persistent AF group and 60 patients in LSPAF group. The pre-procedure LA diameter and volume were smaller in the paroxysmal AF group than persistent AF and LSPAF group. There was no significant change of in the LA structure and function in the paroxysmal AF group within 1 year. In the persistent AF and LSPAF groups, LA structure (anteroposterior diameter, LA volume) significantly decreased, but remained larger than that in paroxysmal AF group. In persistent and LSPAF, function (LA ejection fraction, strain, strain rate) increased significantly within 1 week, then gradually increased. RFCA improved the LA structure and function and resulted in heart reverse remodeling, especially for persistent AF and LSPAF.

© 2020-Center for Economic Integration, Sejong Institution, Sejong University, All Rights Reserved. The Association of Southeast Asia Nations (ASEAN) Power Grid (APG) is an interesting case study on how and why beneficial economic integration may not proceed smoothly. This paper explores the discrepancy between the feasible and beneficial prosperity and the current modest progress for the APG, and its reasons and possible solutions informed by lessons from European power market integration. It compares Europe's experiences in energy market integration with the ASEAN's and identifies two models of regional power connectivity: the European Union (EU) and the Nordic Power Pool, respectively. The bottom-up approach in the Nordic Power Pool suggests that the ASEAN could still promote regional power connectivity even without a supranational authority as in the EU. The paper further suggests that the APG should not be limited by political boundaries. Full benefit recognition, cost-benefit sharing, soft enforcement mechanisms, and human capacity building are the ways forward for the ASEAN to attain power connectivity.

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug‐gene‐adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene‐ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert‐gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage.

Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.

This paper assesses how China’s Belt and Road Initiative (BRI) can bring investment and technology into ASEAN and its impact on ASEAN’s electricity market. With the theoretical framework of scenario analysis, this paper provides three scenarios for ASEAN electricity market integration: the Lao PDR–Thailand–Malaysia–Singapore (LTMS) arrangement, a power exchange between heterogeneous grids in the Greater Mekong Sub-region (GMS) region, and a fully competitive electricity market with a fully-developed ASEAN Power Grid (APG). Based on these scenarios, the paper examines the extent to which the BRI can assist electricity market integration in the ASEAN region. It is found that BRI can best fit with and facilitate the first scenario. Given the uncertain outlook for the electricity market integration in the GMS region, how China’s BRI can play a role in facilitating the GMS electricity market integration is not clear under the second scenario. Further, BRI may be less helpful in the third scenario in which the region’s master plan of APG would be realized. In any sense, how BRI can fit with and facilitate ASEAN’s electricity market integration would more depend on the host countries rather than the Chinese side.

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

China's Belt and Road Initiative (BRI) has attracted the world's attention since it was proposed. This study focuses on the BRI in the energy sector in ASEAN. It examines the current status of China's investment into ASEAN's energy sector. It then discusses the problems of China's investment into the ASEAN energy sector with two cases in Myanmar. In addressing the problems and challenges, the study adopts a ‘sense of ownership’ framework to analyze how to improve the acceptability of the Chinese investment. It argues that the BRI priorities, including ‘people-to-people bond’, provide an opportunity to bring a sense of ownership to the community networking that is to be built as a key part of BRI and more profoundly, an opportunity to establish a good image and to make investment projects successful. Keywords: Belt and Road Initiative, ASEAN, Energy investment, Sense of ownership

Objective Epilepsy is among the most common chronic neurologic diseases. There is a need for more data on patient perspectives of treatment to guide patient-centered care initiatives. Patients with epilepsy share their experiences on social media anonymously, but little is known about those discussions. Our aim was to learn what patients with epilepsy discuss regarding their condition and identify treatment-related themes from online patient support groups. Methods A total of 355,838 posts were collected from three online support groups for patients with epilepsy through a crawling script, and an analytical pipeline was built to identify patient conversation content through leveraging of multiple text mining methods. Results were also displayed by network visualization methods. Results Patients with epilepsy sought information about medical treatments, shared their treatment experiences, and sought help from other posters. Key themes related to treatments included the search for optimal personalized treatment strategies as well as identifying and coping with adverse effects. Significance This study showed the feasibility of learning about concerns of patients with epilepsy, especially treatment issues, through text mining methods. However, some manual selection and filtering were necessary to ensure quality results for the treatment analysis. Providers should be aware of online discussions and use analyses of such discussions to help guide effective patient engagement during care.

KDD Health Day/DSHealth 2021, aims to build on the success of the past 3 years to further catalyze the development of links between academic and commercial data science groups and the rapidly developing translational medicine informatics community. The workshop will stimulate discussion as to strategic areas for development and will lead to future cross-disciplinary collaborations. In accordance with the multi-year goal to continue fostering this community as a series of KDD workshops via timely topics, this year the workshop will focus on the state of explainability and trustworthiness in healthcare. The workshop invites full papers, as well as work-in-progress on the application of data science in healthcare. The workshop will feature 8 invited talks from eminent speakers across academia, industry, clinical researchers, and governmental regulatory bodies. In addition, selected papers will be invited to publish in a special issue of Artificial Intelligence in Medicine journal. The summary gives a brief description of the full-day workshop to be held on August, 2021 virtually.

This paper presents a methodology for topic extraction from a corpus of unstructured posts submitted to the online health forum r/Cancer. Topic extraction is important in many fields. It can provide an understanding of how patients and their caregivers manage the disease and related treatments. Reduced vector embeddings are generated for each post using a combination of a pre-trained language model and dimensionality reduction. These embeddings are then clustered with particle swarm optimization (PSO). An embedding size of 300 produced a topic model with a quality of 0.44. This quality level was obtained without biasing the words in the vocabulary towards a specific topic as currently practiced.

BACKGROUND Patient-clinician secure messaging is an important function in patient portals and enables patients and clinicians to communicate on a wide spectrum of issues in a timely manner. With its growing adoption and patient engagement, it is time to comprehensively study the secure messages and user behaviors in order to improve patient-centered care. OBJECTIVE The aim of this paper was to analyze the secure messages sent by patients and clinicians in a large multispecialty health system at Mayo Clinic, Rochester. METHODS We performed message-based, sender-based, and thread-based analyses of more than 5 million secure messages between 2010 and 2017. We summarized the message volumes, patient and clinician population sizes, message counts per patient or clinician, as well as the trends of message volumes and user counts over the years. In addition, we calculated the time distribution of clinician-sent messages to understand their workloads at different times of a day. We also analyzed the time delay in clinician responses to patient messages to assess their communication efficiency and the back-and-forth rounds to estimate the communication complexity. RESULTS During 2010-2017, the patient portal at Mayo Clinic, Rochester experienced a significant growth in terms of the count of patient users and the total number of secure messages sent by patients and clinicians. Three clinician categories, namely “physician—primary care,” “registered nurse—specialty,” and “physician—specialty,” bore the majority of message volume increase. The patient portal also demonstrated growing trends in message counts per patient and clinician. The “nurse practitioner or physician assistant—primary care” and “physician—primary care” categories had the heaviest per-clinician workload each year. Most messages by the clinicians were sent from 7 AM to 5 PM during a day. Yet, between 5 PM and 7 PM, the physicians sent 7.0% (95,785/1,377,006) of their daily messages, and the nurse practitioner or physician assistant sent 5.4% (22,121/408,526) of their daily messages. The clinicians replied to 72.2% (1,272,069/1,761,739) patient messages within 1 day and 90.6% (1,595,702/1,761,739) within 3 days. In 95.1% (1,499,316/1,576,205) of the message threads, the patients communicated with their clinicians back and forth for no more than 4 rounds. CONCLUSIONS Our study found steady increases in patient adoption of the secure messaging system and the average workload per clinician over 8 years. However, most clinicians responded timely to meet the patients’ needs. Our study also revealed differential patient-clinician communication patterns across different practice roles and care settings. These findings suggest opportunities for care teams to optimize messaging tasks and to balance the workload for optimal efficiency.

BACKGROUND Patient portals tethered to electronic health records systems have become attractive web platforms since the enacting of the Medicare Access and Children’s Health Insurance Program Reauthorization Act and the introduction of the Meaningful Use program in the United States. Patients can conveniently access their health records and seek consultation from providers through secure web portals. With increasing adoption and patient engagement, the volume of patient secure messages has risen substantially, which opens up new research and development opportunities for patient-centered care. OBJECTIVE This study aims to develop a data model for patient secure messages based on the Fast Healthcare Interoperability Resources (FHIR) standard to identify and extract significant information. METHODS We initiated the first draft of the data model by analyzing FHIR and manually reviewing 100 sentences randomly sampled from more than 2 million patient-generated secure messages obtained from the online patient portal at the Mayo Clinic Rochester between February 18, 2010, and December 31, 2017. We then annotated additional sets of 100 randomly selected sentences using the Multi-purpose Annotation Environment tool and updated the data model and annotation guideline iteratively until the interannotator agreement was satisfactory. We then created a larger corpus by annotating 1200 randomly selected sentences and calculated the frequency of the identified medical concepts in these sentences. Finally, we performed topic modeling analysis to learn the hidden topics of patient secure messages related to 3 highly mentioned microconcepts, namely, fatigue, prednisone, and patient visit, and to evaluate the proposed data model independently. RESULTS The proposed data model has a 3-level hierarchical structure of health system concepts, including 3 macroconcepts, 28 mesoconcepts, and 85 microconcepts. Foundation and base macroconcepts comprise 33.99% (841/2474), clinical macroconcepts comprise 64.38% (1593/2474), and financial macroconcepts comprise 1.61% (40/2474) of the annotated corpus. The top 3 mesoconcepts among the 28 mesoconcepts are condition (505/2474, 20.41%), medication (424/2474, 17.13%), and practitioner (243/2474, 9.82%). Topic modeling identified hidden topics of patient secure messages related to fatigue, prednisone, and patient visit. A total of 89.2% (107/120) of the top-ranked topic keywords are actually the health concepts of the data model. CONCLUSIONS Our data model and annotated corpus enable us to identify and understand important medical concepts in patient secure messages and prepare us for further natural language processing analysis of such free texts. The data model could be potentially used to automatically identify other types of patient narratives, such as those in various social media and patient forums. In the future, we plan to develop a machine learning and natural language processing solution to enable automatic triaging solutions to reduce the workload of clinicians and perform more granular content analysis to understand patients’ needs and improve patient-centered care.

BACKGROUND Genealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. Researchers have used policyholder and their dependent information in medical claims data and emergency contacts in electronic health records (EHRs) to infer family relationships at a large scale. We have previously demonstrated that online obituaries can be a novel data source for building more complete and accurate family trees. OBJECTIVE Aiming at supplementing EHR data with family relationships for biomedical research, we built an end-to-end information extraction system using a multitask-based artificial neural network model to construct genealogical knowledge graphs (GKGs) from online obituaries. GKGs are enriched family trees with detailed information including age, gender, death and birth dates, and residence. METHODS Built on a predefined family relationship map consisting of 4 types of entities (eg, people’s name, residence, birth date, and death date) and 71 types of relationships, we curated a corpus containing 1700 online obituaries from the metropolitan area of Minneapolis and St Paul in Minnesota. We also adopted data augmentation technology to generate additional synthetic data to alleviate the issue of data scarcity for rare family relationships. A multitask-based artificial neural network model was then built to simultaneously detect names, extract relationships between them, and assign attributes (eg, birth dates and death dates, residence, age, and gender) to each individual. In the end, we assemble related GKGs into larger ones by identifying people appearing in multiple obituaries. RESULTS Our system achieved satisfying precision (94.79%), recall (91.45%), and F-1 measures (93.09%) on 10-fold cross-validation. We also constructed 12,407 GKGs, with the largest one made up of 4 generations and 30 people. CONCLUSIONS In this work, we discussed the meaning of GKGs for biomedical research, presented a new version of a corpus with a predefined family relationship map and augmented training data, and proposed a multitask deep neural system to construct and assemble GKGs. The results show our system can extract and demonstrate the potential of enriching EHR data for more genetic research. We share the source codes and system with the entire scientific community on GitHub without the corpus for privacy protection.

Sequence alignment methods have the promise to reserve important temporal information in electronic health records (EHRs) for comparing patient medical records. Compared to global sequence alignment, local sequence alignment is more useful when comparing patient medical records. One commonly used local sequence alignment algorithm is Smith-Waterman algorithm (SWA), which is widely used for aligning biological sequence. However directly applying this algorithm to align patient medical records will obtain suboptimal performance since it fails to consider complex situations in EHRs such as the temporality of medical events. In this work, we propose a new algorithm called Knowledge-Enriched Local Sequence Alignment algorithm (KELSA), which incorporates meaningful medical knowledge during sequence alignments. We evaluate our algorithm by comparing it to SWA on synthetic EHR data where the reference alignments are known. Our results show that KELSA aligns better than SWA by inserting new daily events and identifying more similarities between patient medical records. Compared to SWA, KELSA is more suitable for locally comparing patient medical records.

Data on disease burden are often used for assessing population health, evaluating the effectiveness of interventions, formulating health policies, and planning future resource allocation. We investigated whether Internet usage and social media data, specifically the search volume on Google, page view count on Wikipedia, and disease mentioning frequency on Twitter, correlated with the disease burden, measured by prevalence and treatment cost, for 1633 diseases over an 11-year period. We also applied least absolute shrinkage and selection operator to predict the burden of diseases. We found that Google search volume is relatively strongly correlated with the burdens for 39 of 1633 diseases, including viral hepatitis, diabetes mellitus, multiple sclerosis, and hemorrhoids. Wikipedia and Twitter data strongly correlated with the burdens of 15 and 7 diseases, respectively. However, an accurate analysis must consider each condition’s characteristics, including acute/chronic nature, severity, familiarity to the public, and the presence of stigma.

Data mapping plays an important role in data integration and exchanges among institutions and organizations with different data standards. However, traditional rule-based approaches and machine learning methods fail to achieve satisfactory results for the data mapping problem. In this paper, we propose a novel and sophisticated deep learning framework for data mapping called mixture feature embedding convolutional neural network (MfeCNN). The MfeCNN model converts the data mapping task to a multiple classification problem. In the model, we incorporated multimodal learning and multiview embedding into a CNN for mixture feature tensor generation and classification prediction. Multimodal features were extracted from various linguistic spaces with a medical natural language processing package. Then, powerful feature embeddings were learned by using the CNN. As many as 10 classes could be simultaneously classified by a softmax prediction layer based on multiview embedding. MfeCNN achieved the best results on unbalanced data (average F1 score, 82.4%) among the traditional state-of-the-art machine learning models and CNN without mixture feature embedding. Our model also outperformed a very deep CNN with 29 layers, which took free texts as inputs. The combination of mixture feature embedding and a deep neural network can achieve high accuracy for data mapping and multiple classification.

The N-terminal acetylation of proteins is a key modification in eukaryotes. However, knowledge of the biological function of N-terminal acetylation modification of proteins in plants is limited. Naa50 is the catalytic subunit of the N-terminal acetyltransferase NatE complex. We previously demonstrated that the absence of Naa50 leads to sterility in Arabidopsis thaliana. In the present study, the lack of Naa50 resulted in collapsed and sterile pollen in Arabidopsis. Further experiments showed that the mutation in Naa50 accelerated programmed cell death in the tapetum. Expression pattern analysis revealed the specific expression of Naa50 in the tapetum cells of anthers at 9-11 stages during pollen development, when tapetal programmed cell death occurred. Reciprocal cross analyses indicated that male sterility in naa50 is caused by sporophytic effects. mRNA sequencing and quantitative PCR of the closed buds showed that the deletion of Naa50 resulted in the upregulation of the cysteine protease coding gene CEP1 and impaired the expression of several genes involved in pollen wall deposition and pollen mitotic division. The collective data suggest that Naa50 balances the degradation of tapetum cells during anther development and plays an important role in pollen development by affecting several pathways.

Background Patient-clinician secure messaging is an important function in patient portals and enables patients and clinicians to communicate on a wide spectrum of issues in a timely manner. With its growing adoption and patient engagement, it is time to comprehensively study the secure messages and user behaviors in order to improve patient-centered care. Objective The aim of this paper was to analyze the secure messages sent by patients and clinicians in a large multispecialty health system at Mayo Clinic, Rochester. Methods We performed message-based, sender-based, and thread-based analyses of more than 5 million secure messages between 2010 and 2017. We summarized the message volumes, patient and clinician population sizes, message counts per patient or clinician, as well as the trends of message volumes and user counts over the years. In addition, we calculated the time distribution of clinician-sent messages to understand their workloads at different times of a day. We also analyzed the time delay in clinician responses to patient messages to assess their communication efficiency and the back-and-forth rounds to estimate the communication complexity. Results During 2010-2017, the patient portal at Mayo Clinic, Rochester experienced a significant growth in terms of the count of patient users and the total number of secure messages sent by patients and clinicians. Three clinician categories, namely “physician—primary care,” “registered nurse—specialty,” and “physician—specialty,” bore the majority of message volume increase. The patient portal also demonstrated growing trends in message counts per patient and clinician. The “nurse practitioner or physician assistant—primary care” and “physician—primary care” categories had the heaviest per-clinician workload each year. Most messages by the clinicians were sent from 7 AM to 5 PM during a day. Yet, between 5 PM and 7 PM, the physicians sent 7.0% (95,785/1,377,006) of their daily messages, and the nurse practitioner or physician assistant sent 5.4% (22,121/408,526) of their daily messages. The clinicians replied to 72.2% (1,272,069/1,761,739) patient messages within 1 day and 90.6% (1,595,702/1,761,739) within 3 days. In 95.1% (1,499,316/1,576,205) of the message threads, the patients communicated with their clinicians back and forth for no more than 4 rounds. Conclusions Our study found steady increases in patient adoption of the secure messaging system and the average workload per clinician over 8 years. However, most clinicians responded timely to meet the patients’ needs. Our study also revealed differential patient-clinician communication patterns across different practice roles and care settings. These findings suggest opportunities for care teams to optimize messaging tasks and to balance the workload for optimal efficiency.

Broader patient-reported experiences in oncology are largely unknown due to the lack of available information from traditional data sources. Online health community data provide an exploratory way to uncover these experiences at a large scale. Analyzing these data can guide further studies towards understanding patients' needs and experiences. However, analysis of online health data is inherently difficult due to the unstructured nature of these data and the variety of ways information can be expressed over text. Specifically, subscribers may not disclose critical information such as the age of the patient in their posts. In fact, the number of health forum posts that explicitly mention the age of the patient is significantly lower than the number of posts that do not include this information in the Reddit r/Cancer health forum under consideration in the present paper. Health-focused studies often need to consider or control for age as a confounder, hence the importance of having sufficient age data. This paper presents a methodology that can help classify health forum posts according to four age groups (0-17, 18-39, 40-64 and 65 + years) even when the posts do not contain explicit mention of the age of the patient. First, the subset of the posts that include explicit mention of the age of the patient is identified. Second, the explicit age clues are removed from these posts and used to train the proposed age classifier. The resulting classifier is able to infer the age of the patient using only implicit age clues with an average true positive rate (TPR) of 71%. This TPR is comparable to the average TPR of 69% obtained from human annotations for the same set of posts.

Background Ulcerative colitis (UC) is an idiopathic intestinal inflammatory disease, which leads to chronic intestinal mucosal barrier damage. More and more evidences show that ubiquitination of proteins regulates the occurrence and development of intestinal inflammation. DCAF family proteins could form E3 ubiquitin ligase with CRL4-DDB1 to regulate cell growth, differentiation, apoptosis and other life activities. CRL4DCAF2 is a crucial regulator in cell cycle regulation, but there are few studies on its application in intestinal epithelium. This study aims to explore the specific mechanism of CRL4DCAF2 in regulating the proliferation and repairment of intestinal epithelial cells. Methods DSS - induced colitis in mice was used as the experimental model in vivo. HCT116 and SW480 cell lines were used as experimental models in vitro studies.The Cre-loxP system was used to construct a mouse model of intestinal epithelium-specific DCAF2 knockout. The intestinal mucosa biopsy specimens of 11 normal patients and 11 UC patients were collected. In addition, qRT-PCR, Western blot, RNA-seq and immunofluorescent staining were used to detect the expression levels of target genes in human colon biopsy specimens, mouse colon tissues, HCT116 or SW480 cells Results DCAF2 gene was highly expressed in the colon of mice. The occurrence and development of DSS-induced experimental colitis was accompanied by a significant down-regulation of DCAF2 protein expression in colon. DCAF2 mRNA level was significantly decreased in UC patients. Mouse with intestinal epithelial-specific knockout of DCAF2(i.e. DCAF2IEC-KO) suffered from embryonic death. Compared with wild-type adult C57BL/6J mice, DCAF2IEC-KD mouse showed more severe intestinal inflammation in DSS-induced colitis model. CCK-8 test, PI staining and EDU staining flow cytometry experiments showed that the proliferation of intestinal epithelial cells with DCAF2 overexpression was faster than that of the control (P < 0.05) in HCT116 and SW480 cell lines, while in knockdown of DCAF2 models, the opposite results were obtained. Its effect may be related to the ubiquitination of p21. At the same time, MLN4924 in vivo and in vitro experiments further verified our experimental results. Combined with RNA-seq and Western blot, we also found that DCAF2 may reduce the symptoms of colitis by maintaining the stability of autophagy. Conclusion DCAF2 is low expressed in patients with ulcerative colitis, which may promote the activation and proliferation of intestinal epithelial cells. It could maintain autophagy stability, and restore intestinal barrier, thus alleviate the development of ulcerative colitis

An advanced functional understanding of omics data is important for elucidating the design logic of physiological processes in plants and effectively controlling desired traits in plants. We present the latest versions of the Predicted Arabidopsis Interactome Resource (PAIR) and of the gene set linkage analysis (GSLA) tool, which enable the interpretation of an observed transcriptomic change (differentially expressed genes [DEGs]) in Arabidopsis (Arabidopsis thaliana) with respect to its functional impact for biological processes. PAIR version 5.0 integrates functional association data between genes in multiple forms and infers 335,301 putative functional interactions. GSLA relies on this high-confidence inferred functional association network to expand our perception of the functional impacts of an observed transcriptomic change. GSLA then interprets the biological significance of the observed DEGs using established biological concepts (annotation terms), describing not only the DEGs themselves but also their potential functional impacts. This unique analytical capability can help researchers gain deeper insights into their experimental results and highlight prospective directions for further investigation. We demonstrate the utility of GSLA with two case studies in which GSLA uncovered how molecular events may have caused physiological changes through their collective functional influence on biological processes. Furthermore, we showed that typical annotation-enrichment tools were unable to produce similar insights to PAIR/GSLA. The PAIR version 5.0-inferred interactome and GSLA Web tool both can be accessed at http://public.synergylab.cn/pair/.

This paper studies how energy security is conceptualized in four resource-poor, advanced island economies: Singapore, South Korea, Japan, and Taiwan. It is found that the energy security conceptualization of the four economies in effect returns to the very original and conventional one: stable and reliable energy supply. However, these economies are different in the level of stability and reliability demanded. Why are they similar in energy security conceptualization whereas different in the level of stability and reliability demanded? Adopting documentation analysis, comparative study, and the varieties of capitalism theory, we find that the nature of economy constitutes the decisive factor that shapes energy security conceptualization. The coordinated market economies (CMEs) are more concerned about energy supply disruption than the liberalized market economies (LMEs). The paper demonstrates that despite numerous energy security concepts in the literature, resource-poor economies still adopt the original and conventional one in practice. The findings suggest that security of supply is the top measure for resource-poor economies to improve their energy security and creating a joint petroleum and LNG market would be desirable for the four economies in this study.

Objective: The present study was to compare the incidence of septal defect (SD) in patients with atrial fibrillation (AF) who received radiofrequency ablation or cryoablation. Methods: A total of 293 AF patients were performed with radiofrequency ablation and cryoablation. Cardiac ultrasonography was performed to calculate left atrial diameter (LAD), left atrial ejection fraction (LAEF%), strain rate (SR), left ventricular systolic (SRs), left ventricular diastolic (SRe), and left atrial systole (SRa) before surgery, 3 months and 1 year after surgery. The patients were followed up to observe statin and angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor blocker (ARB) medication, AF recurrence, 6-min walk test, stroke, any symptoms caused by arrhythmia, and re-hospitalization. Results: The levels of LAD and SD were higher, while SRe and SRa were lower in the cryoablation group in the comparison with the radiofrequency ablation group after surgery (P

Atrial fibrillation (AF) is a commonly encountered heart arrhythmia and a risk factor for cardiovascular system. The purpose of the present study was to explore the role of long non-coding RNA myocardial infarction-associated transcript (MIAT) in AF and AF-induced myocardial fibrosis and the possible mechanisms involved in this process. We successfully induced an AF rat model. Expression of MIAT presented a dramatic increase, while microRNA (miR)-133a-3p presented a dramatic decrease in atrium tissues of rats with AF induction. In addition, we also found that MIAT was highly expressed and miR-133a-3p was significantly reduced in peripheral blood leukocyte of AF patients. For biological function exploration of MIAT/miR-133a-3p axis, MIAT was knocked down using small hairpin RNA (shRNA) lentivirus injection and the rescue experiments were performed simultaneously by inhibiting miR-133a-3p using anti-miR-133a-3p lentivirus injection in rats with AF. MIAT downregulation significantly alleviated AF, increased atrial effective refractory period (AERP), and reduced the duration of AF as well as cardiomyocytes apoptosis. Whereas these effects of MIAT downregulation on AF were reversed by anti-miR-133a-3p administration. Luciferase reporter revealed that miR-133a-3p was directly regulated by MIAT. Moreover, MIAT knockdown effectively reduced AF-induced atrial fibrosis by detecting reduced collagen in the right atria and inhibited expression of fibrosis-related gene expression of collagen I, collagen III, connective tissue growth factor (CTGF) and transforming growth factor-β1 (TGF-β1) in rats with AF, these findings were in contrast with the findings for rats with inhibition of miR-133a-3p. In conclusion, our study demonstrated the role of MIAT downregulation in alleviating AF and AF-induced myocardial fibrosis, and the functional regulatory pathway of MIAT targeting miR-133a-3p.

Background Accurate information in provider directories are vital in health care including health information exchange, health benefits exchange, quality reporting, and in the reimbursement and delivery of care. Maintaining provider directory data and keeping it up to date is challenging. The objective of this study is to determine the feasibility of using natural language processing (NLP) techniques to combine disparate resources and acquire accurate information on health providers. Methods Publically available state licensure lists in Connecticut were obtained along with National Plan and Provider Enumeration System (NPPES) public use files. Connecticut licensure lists textual information of each health professional who is licensed to practice within the state. A NLP-based system was developed based on healthcare provider taxonomy code, location, name and address information to identify textual data within the state and federal records. Qualitative and quantitative evaluation were performed, and the recall and precision were calculated. Results We identified nurse midwives, nurse practitioners, and dentists in the State of Connecticut. The recall and precision were 0.95 and 0.93 respectively. Using the system, we were able to accurately acquire 6849 of the 7177 records of health provider directory information. Conclusions The authors demonstrated that the NLP- based approach was effective at acquiring health provider information. Furthermore, the NLP-based system can always be applied to update information further reducing processing burdens as data changes.

Background Genealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. Researchers have used policyholder and their dependent information in medical claims data and emergency contacts in electronic health records (EHRs) to infer family relationships at a large scale. We have previously demonstrated that online obituaries can be a novel data source for building more complete and accurate family trees. Objective Aiming at supplementing EHR data with family relationships for biomedical research, we built an end-to-end information extraction system using a multitask-based artificial neural network model to construct genealogical knowledge graphs (GKGs) from online obituaries. GKGs are enriched family trees with detailed information including age, gender, death and birth dates, and residence. Methods Built on a predefined family relationship map consisting of 4 types of entities (eg, people’s name, residence, birth date, and death date) and 71 types of relationships, we curated a corpus containing 1700 online obituaries from the metropolitan area of Minneapolis and St Paul in Minnesota. We also adopted data augmentation technology to generate additional synthetic data to alleviate the issue of data scarcity for rare family relationships. A multitask-based artificial neural network model was then built to simultaneously detect names, extract relationships between them, and assign attributes (eg, birth dates and death dates, residence, age, and gender) to each individual. In the end, we assemble related GKGs into larger ones by identifying people appearing in multiple obituaries. Results Our system achieved satisfying precision (94.79%), recall (91.45%), and F-1 measures (93.09%) on 10-fold cross-validation. We also constructed 12,407 GKGs, with the largest one made up of 4 generations and 30 people. Conclusions In this work, we discussed the meaning of GKGs for biomedical research, presented a new version of a corpus with a predefined family relationship map and augmented training data, and proposed a multitask deep neural system to construct and assemble GKGs. The results show our system can extract and demonstrate the potential of enriching EHR data for more genetic research. We share the source codes and system with the entire scientific community on GitHub without the corpus for privacy protection.

Background Patient portals tethered to electronic health records systems have become attractive web platforms since the enacting of the Medicare Access and Children’s Health Insurance Program Reauthorization Act and the introduction of the Meaningful Use program in the United States. Patients can conveniently access their health records and seek consultation from providers through secure web portals. With increasing adoption and patient engagement, the volume of patient secure messages has risen substantially, which opens up new research and development opportunities for patient-centered care. Objective This study aims to develop a data model for patient secure messages based on the Fast Healthcare Interoperability Resources (FHIR) standard to identify and extract significant information. Methods We initiated the first draft of the data model by analyzing FHIR and manually reviewing 100 sentences randomly sampled from more than 2 million patient-generated secure messages obtained from the online patient portal at the Mayo Clinic Rochester between February 18, 2010, and December 31, 2017. We then annotated additional sets of 100 randomly selected sentences using the Multi-purpose Annotation Environment tool and updated the data model and annotation guideline iteratively until the interannotator agreement was satisfactory. We then created a larger corpus by annotating 1200 randomly selected sentences and calculated the frequency of the identified medical concepts in these sentences. Finally, we performed topic modeling analysis to learn the hidden topics of patient secure messages related to 3 highly mentioned microconcepts, namely, fatigue, prednisone, and patient visit, and to evaluate the proposed data model independently. Results The proposed data model has a 3-level hierarchical structure of health system concepts, including 3 macroconcepts, 28 mesoconcepts, and 85 microconcepts. Foundation and base macroconcepts comprise 33.99% (841/2474), clinical macroconcepts comprise 64.38% (1593/2474), and financial macroconcepts comprise 1.61% (40/2474) of the annotated corpus. The top 3 mesoconcepts among the 28 mesoconcepts are condition (505/2474, 20.41%), medication (424/2474, 17.13%), and practitioner (243/2474, 9.82%). Topic modeling identified hidden topics of patient secure messages related to fatigue, prednisone, and patient visit. A total of 89.2% (107/120) of the top-ranked topic keywords are actually the health concepts of the data model. Conclusions Our data model and annotated corpus enable us to identify and understand important medical concepts in patient secure messages and prepare us for further natural language processing analysis of such free texts. The data model could be potentially used to automatically identify other types of patient narratives, such as those in various social media and patient forums. In the future, we plan to develop a machine learning and natural language processing solution to enable automatic triaging solutions to reduce the workload of clinicians and perform more granular content analysis to understand patients’ needs and improve patient-centered care.

Renewable off-grid solution plays a critical role in supporting rural electrification. However, off-grid Renewable Energy (OGRE) project financing faces significant challenges due to limited financing access, low affordability of consumers, high transactions costs and etc. Various supporting instruments have been implemented to facilitate OGRE investment. This study assesses the effectiveness of those instruments with a framework consists of three dimensions: feasibility, sustainability and replicability. The weights of each dimension in the framework and the scores of each instrument are assessed by expert surveys based on the Delphi method. It is suggested that all the three dimensions should be taken into consideration while assessing the instruments, among which feasibility and sustainability are considered as the most important dimensions in the assessment framework. Furthermore, the top-5 most effective instruments in facilitating OGRE investment are local engagement in operation and maintenance, loan guarantee, start-up grant, end user financing, and concessional finance. Developing countries that need to increase electrification, such as most of the ASEAN member states, could use these top scored instruments despite of their limited amount of public finance.

© 2016 Elsevier B.V. This study proposes to aggregately measure energy security performance with the principal component analysis. In its application of the methodology to four resource-poor yet economically advanced island economies in East Asia—Singapore, South Korea, Japan, and Taiwan, this study establishes a novel framework to conceptualize energy security. The framework incorporates three dimensions: vulnerability, efficiency, and sustainability, three indicators being allocated to each dimension. The study finds that all the three dimensions are critical for the resource-poor economies but have different weights in each of them. An urgent task for these four economies is to implement energy efficiency and conservation measures. Liberalization of electricity sector can be a helpful tool to reduce energy consumption and increase efficiency. All of them have been committed to promoting renewable energy development, which shall be further expanded in these economies.