Measuring the size of the shadow economy using a dynamic general equilibrium model with trends: a new dataset

We provide estimates of the size and dollar value of shadow economy for a set of countries between 1950 and 2015, following the methodology of Solis-Garcia and Xie (2018)

Measuring the size of the shadow economy using a dynamic general equilibrium model with trends: a new dataset

We provide estimates of the size and dollar value of shadow economy for a set of countries between 1950 and 2015, following the methodology of Solis-Garcia and Xie (2018)

Development and Validation of an Attitudinal-Profiling Tool for Patients With Asthma

This study was supported and funded by Mundipharma Pte Ltd. Online survey and statistical analysis were performed by Pei-Li Teh, Rachel Howard, Tsin-Li Chua and Jie Sun of Research Partnership Pte Ltd. Medical writing support was provided by Sen-Kwan Tay of Research2Trials Clinical Solutions Pte Ltd. The authors received honoraria from Mundipharma for their participation in the REALISE Asia Working Group meetings and discussions. Prof Price has Board membership with Mundipharma; and had received consultancy and speaker fees, grants and unrestricted funding support from Mundipharma; and payment for manuscript preparation and travel/accommodations/meeting expenses from Mundipharma. Profs Liam and David-Wang are members of the Asia-Pacific Advisory Board of Mundipharma. Profs Cho and David-Wang had received speaker fees from Mundipharma in the past. Dr Neira was an employee of Mundipharma Pte Ltd, Singapore. Ms Teh is an employee of Research Partnership Pte Ltd which conducted the REALISE Asia survey for Mundipharma. Prof Cho is a member of the Editorial Board of Allergy, Asthma & Immunology.Peer reviewedPublisher PD

Time for a new language for asthma control : Results from REALISE Asia

Acknowledgments: This study was supported and funded by Mundipharma Pte Ltd. Online survey and statistical analysis were performed by Pei-Li Teh, Rachel Howard, Tsin-Li Chua, and Jie Sun of Research Partnership Pte Ltd. Medical writing support was provided by Sen-Kwan Tay of Research2Trials Clinical Solutions Pte Ltd.Peer reviewedPublisher PD

Tumor detection using IRIS pattern.

Cancer was the top 10 killer in the world. WHO reported that in years 2000, death cause by cancer patient is 6.2 million people worldwide (WHO, 2003). The earlier cancer patient know he was infected by cancer would give him higher chances to cure it. It is event better if we can predict and prevent it rather than curing it. With iridology, we could analyze cell and body activities (Lindlahr, 1919). When cells growth abnormally, iris will show some sign and changes that iridologist could tell it tumor stated to grow (Lindlahr, 1919). Thus this could prevent the tumor from grow or grow into second stage that is cancer

Iris recognition using self-organizing neural network

Among biometric systems for user verification, iris recognition systems represent a relatively new technology. Our system consists of two main parts: a localizing iris and iris pattern recognition. The raw image is captured using a digital camera. The iris is then extracted from the background after enhancement and noise elimination. Due to noise and the high degree of freedom in the iris pattern, only parts of the iris structure are selected for recognition. The selected iris structure is then reconstructed into a rectangle format. Using a trained self-organizing map neural network, iris patterns are recognized. The overall accuracy of our network is found to be about 83%

Data resource profile: the National Health Insurance Research Database (NHIRD).

Electronic health records (EHRs) can provide researchers with extraordinary opportunities for population-based research. The National Health Insurance system of Taiwan was established in 1995 and covers more than 99.6% of the Taiwanese population; this system's claims data are released as the National Health Insurance Research Database (NHIRD). All data from primary outpatient departments and inpatient hospital care settings after 2000 are included in this database. After a change and update in 2016, the NHIRD is maintained and regulated by the Data Science Centre of the Ministry of Health and Welfare of Taiwan. Datasets for approved research are released in three forms: sampling datasets comprising 2 million subjects, disease-specific databases, and full population datasets. These datasets are de-identified and contain basic demographic information, disease diagnoses, prescriptions, operations, and investigations. Data can be linked to government surveys or other research datasets. While only a small number of validation studies with small sample sizes have been undertaken, they have generally reported positive predictive values of over 70% for various diagnoses. Currently, patients cannot opt out of inclusion in the database, although this requirement is under review. In conclusion, the NHIRD is a large, powerful data source for biomedical research

DoDo Learning: DOmain-DemOgraphic Transfer in Language Models for Detecting Abuse Targeted at Public Figures

Public figures receive a disproportionate amount of abuse on social media, impacting their active participation in public life. Automated systems can identify abuse at scale but labelling training data is expensive, complex and potentially harmful. So, it is desirable that systems are efficient and generalisable, handling both shared and specific aspects of online abuse. We explore the dynamics of cross-group text classification in order to understand how well classifiers trained on one domain or demographic can transfer to others, with a view to building more generalisable abuse classifiers. We fine-tune language models to classify tweets targeted at public figures across DOmains (sport and politics) and DemOgraphics (women and men) using our novel DODO dataset, containing 28,000 labelled entries, split equally across four domain-demographic pairs. We find that (i) small amounts of diverse data are hugely beneficial to generalisation and model adaptation; (ii) models transfer more easily across demographics but models trained on cross-domain data are more generalisable; (iii) some groups contribute more to generalisability than others; and (iv) dataset similarity is a signal of transferability.Comment: 15 pages, 7 figures, 4 table

Cardiovascular outcomes associated with use of clarithromycin: population based study

Study question What is the association between clarithromycin use and cardiovascular outcomes? Methods In this population based study the authors compared cardiovascular outcomes in adults aged 18 or more receiving oral clarithromycin or amoxicillin during 2005-09 in Hong Kong. Based on age within five years, sex, and calendar year at use, each clarithromycin user was matched to one or two amoxicillin users. The cohort analysis included patients who received clarithromycin (n=108 988) or amoxicillin (n=217 793). The self controlled case series and case crossover analysis included those who received Helicobacter pylori eradication treatment containing clarithromycin. The primary outcome was myocardial infarction. Secondary outcomes were all cause, cardiac, or non-cardiac mortality, arrhythmia, and stroke. Study answer and limitations The propensity score adjusted rate ratio of myocardial infarction 14 days after the start of antibiotic treatment was 3.66 (95% confidence interval 2.82 to 4.76) comparing clarithromycin use (132 events, rate 44.4 per 1000 person years) with amoxicillin use (149 events, 19.2 per 1000 person years), but no long term increased risk was observed. Similarly, rate ratios of secondary outcomes increased significantly only with current use of clarithromycin versus amoxicillin, except for stroke. In the self controlled case analysis, there was an association between current use of H pylori eradication treatment containing clarithromycin and cardiovascular events. The risk returned to baseline after treatment had ended. The case crossover analysis also showed an increased risk of cardiovascular events during current use of H pylori eradication treatment containing clarithromycin. The adjusted absolute risk difference for current use of clarithromycin versus amoxicillin was 1.90 excess myocardial infarction events (95% confidence interval 1.30 to 2.68) per 1000 patients. What this study adds Current use of clarithromycin was associated with an increased risk of myocardial infarction, arrhythmia, and cardiac mortality short term but no association with long term cardiovascular risks among the Hong Kong population