Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

<p>Abstract</p> <p>Background</p> <p>Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns.</p> <p>Methods</p> <p>In this study, we utilize spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters, and areas in which these are situated, for the 20 leading causes of death in Taiwan. In addition, we use the fit to a logistic regression model to test the characteristics of similarity and dissimilarity by gender.</p> <p>Results</p> <p>Gender is compared in efforts to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis is utilized to identify spatial cluster patterns. There is naturally great interest in discovering the relationship between the leading causes of death and well-documented spatial risk factors. For example, in Taiwan, we found the geographical distribution of clusters where there is a prevalence of tuberculosis to closely correspond to the location of aboriginal townships.</p> <p>Conclusions</p> <p>Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations for leading health care events. This is of great aid in assessing spatial risk factors, which in turn facilitates the planning of the most advantageous types of health care policies and implementation of effective health care services.</p

Identification of Patients with Potential Atrial Fibrillation during Sinus Rhythm Using Isolated P Wave Characteristics from 12-Lead ECGs

Atrial fibrillation (AF) is largely underdiagnosed. Previous studies using deep neural networks with large datasets have shown that screening AF with a 12-lead electrocardiogram (ECG) during sinus rhythm (SR) is possible. However, the poor availability of these trained models and the small size of the retrievable datasets limit its reproducibility. This study proposes an approach to generate explainable features for detecting AF during SR with limited data. We collected 94,224 12-lead ECGs from 64,196 patients from Taipei Medical University Hospital. We selected ECGs during SR from 213 patients before AF diagnosis and randomly selected 247 age-matched participants without AF records as the controls. We developed a signal-processing technique, MA-UPEMD, to isolate P waves, and quantified the spatial and temporal features using principal component analysis and inter-lead relationships. By combining these features, the machine learning models yielded AUC of 0.64. We showed that, even with this limited dataset, the P wave, representing atrial electrical activity, is depicted by our proposed approach. The extracted features performed better than the bandpass filter-extracted P waves and deep neural network model. We provided a physiologically explainable and reproducible approach for classifying patients with AF during SR

A Plausible Model for the Galactic Extended Red Emission: Graphene Exposed to Far-ultraviolet Light

Extended red emission (ERE) is a broad feature in the spectral region of 500–900 nm commonly observed in a wide range of circumstellar and interstellar environments. Although the observational constraints for ERE are well established, definitive identifications of the carriers and associated processes complying with these constraints remain unanswered. We report a plausible two-step model involving far-ultraviolet (UV)-irradiated single-layer graphene (SLG), considered as large polycyclic aromatic hydrocarbons, to meet these constraints and supported by laboratory experiments. The far-UV-treated SLG, producing structural defects and graphene quantum dots, showed photoluminescence excitation spectrum extending from the far-UV to UV–visible region, hence meeting the requirements of far-UV light and high photon conversion efficiency. Furthermore, a photoluminescence band shifted from ∼585 to ∼750 nm for high-dose-exposed SLG agrees with the observed redshift of the ERE band in regions under a greater far-UV radiation density