Guest Editorial Cardiovascular Health Informatics: Risk Screening and Intervention

Despite enormous efforts to prevent cardiovascular disease (CVD) in the past, it remains the leading cause of death in most countries worldwide. Around two-thirds of these deaths are due to acute events, which frequently occur suddenly and are often fatal beforemedical care can be given. New strategies for screening and early intervening CVD, in addition to the conventional methods, are therefore needed in order to provide personalized and pervasive healthcare. In this special issue, selected emerging technologies in health informatics for screening and intervening CVDs are reported. These papers include reviews or original contributions on 1) new potential genetic biomarkers for screening CVD outcomes and high-throughput techniques for mining genomic data; 2) new imaging techniques for obtaining faster and higher resolution images of cardiovascular imaging biomarkers such as the cardiac chambers and atherosclerotic plaques in coronary arteries, as well as possible automatic segmentation, identification, or fusion algorithms; 3) new physiological biomarkers and novel wearable and home healthcare technologies for monitoring them in daily lives; 4) new personalized prediction models of plaque formation and progression or CVD outcomes; and 5) quantifiable indices and wearable systems to measure them for early intervention of CVD through lifestyle changes. It is hoped that the proposed technologies and systems covered in this special issue can result in improved CVD management and treatment at the point of need, offering a better quality of life to the patient

Leaf segmentation and tracking using probabilistic parametric active contours

Active contours or snakes are widely used for segmentation and tracking. These techniques require the minimization of an energy function, which is generally a linear combination of a data fit term and a regularization term. This energy function can be adjusted to exploit the intrinsic object and image features. This can be done by changing the weighting parameters of the data fit and regularization term. There is, however, no rule to set these parameters optimally for a given application. This results in trial and error parameter estimation. In this paper, we propose a new active contour framework defined using probability theory. With this new technique there is no need for ad hoc parameter setting, since it uses probability distributions, which can be learned from a given training dataset

Accelerated carbonation of reactive MgO and Portland cement blends under flowing CO2 gas

The use of MgO-based materials for sequestration of CO2 offers technical advantages and environmental incentives. However, the understanding of accelerated carbonation of MgO-based materials in flowing CO2 gas is limited. This study elucidates the carbonation behaviour of reactive MgO cement (MC) and MgO-Portland binary cement (BC) in a simulated CO2-rich industrial exhaust. Quantitative X-ray diffraction and thermogravimetric analyses showed that nesquehonite (MgCO3·3H2O) was the major carbonation product in MC pastes, whereas CaCO3 was preferentially generated in BC pastes. The relative humidity of exhaust gas influenced CO2 diffusion and the carbonation rate; 98% humidity facilitated MC carbonation whereas 50% was favourable for BC carbonation. Although CO2 concentration determined the carbonation rate, 10% CO2 gas in the exhaust was sufficient to accelerate carbonation. The carbonation degree and compressive strength of samples cured for 7 days at 10% CO2 were comparable to the values of samples cured for 1 day at 100% CO2. The presence of acid gases during CO2 curing inhibited the carbonation and hydration processes, but the presence of Portland cement in the BC system gave good compatibility with acids and relieved the inhibitory effect. Desulphurization and denitrification of industrial exhaust gas are nonetheless desirable before CO2 curing. This study builds the foundation for utilising industrial CO2 exhaust to accelerate the carbonation of Mg-based materials

Rms-flux relation in the optical fast variability data of BL Lacertae object S5 0716+714

The possibility that BL Lac S5 0716+714 exhibits a linear root mean square (rms)-flux relation in its IntraDay Variability (IDV) is analysed. The results may be used as an argument in the existing debate regarding the source of optical IDV in Active Galactic Nuclei. 63 time series in different optical bands were used. A linear rms-flux relation at a confidence level higher than 65% was recovered for less than 8% of the cases. We were able to check if the magnitude is log-normally distributed for eight timeseries and found, with a confidence > 95%, that this is not the case.Comment: Accepted by Astrophysics and Space Scienc

Benefits and limitations of implementing Chronic Care Model (CCM) in primary care programs: a systematic review

Background: Chronic Care Model (CCM) has been developed to improve patients' health care by restructuring health systems in a multidimensional manner. This systematic review aims to summarize and analyse programs specifically designed and conducted for the fulfilment of multiple CCM components. We have focused on programs targeting diabetes mellitus, hypertension and cardiovascular disease. Method and results: This review was based on a comprehensive literature search of articles in the PubMed database that reported clinical outcomes. We included a total of 25 eligible articles. Evidence of improvement in medical outcomes and the compliance of patients with medical treatment were reported in 18 and 14 studies, respectively. Two studies demonstrated a reduction of the medical burden in terms of health service utilization, and another two studies reported the effectiveness of the programs in reducing the risk of heart failure and other cardiovascular diseases. However, CCMs were still restricted by limited academic robustness and social constraints when they were implemented in primary care. Higher professional recognition, tighter system collaborations and increased financial support may be necessary to overcome the limitations of, and barriers to CCM implementation. Conclusion: This review has identified the benefits of implementing CCM, and recommended suggestions for the future development of CCM

Reproducible Cancer Biomarker Discovery in SELDI-TOF MS Using Different Pre-Processing Algorithms

BACKGROUND: There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS) studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached. RESULTS: In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE) peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consistency of DE peak identification using different algorithms. One factor is that some DE peaks selected from one peak profile were not detected as peaks in other profiles, and the second factor is that the statistical power of identifying DE peaks in large peak profiles with many peaks may be low due to the large scale of the tests and small number of samples. Furthermore, we demonstrated that the DE peak detection power in large profiles could be improved by the stratified false discovery rate (FDR) control approach and that the reproducibility of DE peak detection could thereby be increased. CONCLUSIONS: Comparing and evaluating pre-processing algorithms in terms of reproducibility can elucidate the relationship among different algorithms and also help in selecting a pre-processing algorithm. The DE peaks selected from small peak profiles with few peaks for a dataset tend to be reproducibly detected in large peak profiles, which suggests that a suitable pre-processing algorithm should be able to produce peaks sufficient for identifying useful and reproducible biomarkers

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele