The public understanding of climate change : A case study of Taiwanese youth

Global climate change is likely to be the most challenging environmental dilemma of the 21st century because its impacts on ecosystems and human society are transnational in scale and long term in scope. Due to its high scientific complexity and uncertainty and high political and economic sensitivity, mitigating the problem will require interdisciplinary cooperation and collective and sustained efforts on the part of all nations. Sufficient domestic support from both government and the lay public will not only be significant to the success of an international climate regime, but also crucial to the effectiveness of potential domestic climate policies. Such circumstances call for exploration of how the level of the public’s scientific understanding of climate change influences choices for climate protective actions and support for climate policies. Social scientists have the responsibility to explore how people perceive, understand, and respond to global climate change and to investigate the roles and interrelationships of various actors (e.g., scientists, citizens, and elected and appointed officials) in the policy-making process. Compared with numerous social scientific studies of global climate change in North America and Europe, substantially fewer investigations have focused on other regions of the world. Therefore, this doctoral research presents a case study of domestic climate policy formulation premised on the integration of science and citizens in an industrialized Asian society - Taiwan. This dissertation reports the views of Taiwanese youth with respect to global climate change based on data compiled from three empirical studies (i.e., integrated assessment focus groups, pre- and post-surveys, and a web-based survey). These studies in combination present three primary findings: 1) Most Taiwanese young adults tend to endorse pro-climate protection attitudes and behaviors; 2) These young adults display an extensive but limited scientific understanding pertaining to the problem; 3) A process of experimental participation with scientists enhanced individual scientific understanding and policy making. Further investigation revealed that these perceptions were grounded in a strong sense of ecological citizenship, which is likely influenced by the contemporary environmental movement in Taiwan since the 1980s. While this case study finds that scientific knowledge is less influential in determining individual behavioral intentions than public attitudes toward climate change, the continual enhancement of public ethical awareness about global climate change provides a helpful approach for policy makers seeking to obtain public support

The pathological effects of CCR2+ inflammatory monocytes are amplified by an IFNAR1-triggered chemokine feedback loop in highly pathogenic influenza infection

Background: Highly pathogenic influenza viruses cause high levels of morbidity, including excessive infiltration of leukocytes into the lungs, high viral loads and a cytokine storm. However, the details of how these pathological features unfold in severe influenza infections remain unclear. Accumulation of Gr1 + CD11b + myeloid cells has been observed in highly pathogenic influenza infections but it is not clear how and why they accumulate in the severely inflamed lung. In this study, we selected this cell population as a target to investigate the extreme inflammatory response during severe influenza infection. Results: We established H1N1 IAV-infected mouse models using three viruses of varying pathogenicity and noted the accumulation of a defined Gr1 + CD11b + myeloid population correlating with the pathogenicity. Herein, we reported that CCR2+ inflammatory monocytes are the major cell compartments in this population. Of note, impaired clearance of the high pathogenicity virus prolonged IFN expression, leading to CCR2+ inflammatory monocytes amplifying their own recruitment via an interferon-alpha/beta receptor 1 (IFNAR1)-triggered chemokine loop. Blockage of IFNAR1-triggered signaling or inhibition of viral replication by Oseltamivir significantly suppresses the expression of CCR2 ligands and reduced the influx of CCR2+ inflammatory monocytes. Furthermore, trafficking of CCR2+ inflammatory monocytes from the bone marrow to the lung was evidenced by a CCR2-dependent chemotaxis. Importantly, leukocyte infiltration, cytokine storm and expression of iNOS were significantly reduced in CCR2-/- mice lacking infiltrating CCR2+ inflammatory monocytes, enhancing the survival of the infected mice. Conclusions: Our results indicated that uncontrolled viral replication leads to excessive production of inflammatory innate immune responses by accumulating CCR2+ inflammatory monocytes, which contribute to the fatal outcomes of high pathogenicity virus infections

Serologic Status for Pandemic (H1N1) 2009 Virus, Taiwan

We studied preexisting immunity to pandemic (H1N1) 2009 virus in persons in Taiwan. A total of 18 (36%) of 50 elderly adults in Taiwan born before 1935 had protective antibodies against currently circulating pandemic (H1N1) 2009 virus. Seasonal influenza vaccines induced antibodies that did not protect against pandemic (H1N1) 2009 virus

Associations of obesity and malnutrition with cardiac remodeling and cardiovascular outcomes in Asian adults:A cohort study

BackgroundObesity, a known risk factor for cardiovascular disease and heart failure (HF), is associated with adverse cardiac remodeling in the general population. Little is known about how nutritional status modifies the relationship between obesity and outcomes. We aimed to investigate the association of obesity and nutritional status with clinical characteristics, echocardiographic changes, and clinical outcomes in the general community.Methods and findingsWe examined 5,300 consecutive asymptomatic Asian participants who were prospectively recruited in a cardiovascular health screening program (mean age 49.6 ± 11.4 years, 64.8% male) between June 2009 to December 2012. Clinical and echocardiographic characteristics were described in participants, stratified by combined subgroups of obesity and nutritional status. Obesity was indexed by body mass index (BMI) (low, ≤25 kg/m2 [lean]; high, >25 kg/m2 [obese]) (WHO-recommended Asian cutoffs). Nutritional status was defined primarily by serum albumin (SA) concentration (low, ConclusionsIn our cohort study among asymptomatic community-based adults in Taiwan, we found that obese individuals with poor nutritional status have the highest comorbidity burden, the most adverse cardiac remodeling, and the least favorable composite outcome

Molecular signature of clinical severity in recovering patients with severe acute respiratory syndrome coronavirus (SARS-CoV)

BACKGROUND: Severe acute respiratory syndrome (SARS), a recent epidemic human disease, is caused by a novel coronavirus (SARS-CoV). First reported in Asia, SARS quickly spread worldwide through international travelling. As of July 2003, the World Health Organization reported a total of 8,437 people afflicted with SARS with a 9.6% mortality rate. Although immunopathological damages may account for the severity of respiratory distress, little is known about how the genome-wide gene expression of the host changes under the attack of SARS-CoV. RESULTS: Based on changes in gene expression of peripheral blood, we identified 52 signature genes that accurately discriminated acute SARS patients from non-SARS controls. While a general suppression of gene expression predominated in SARS-infected blood, several genes including those involved in innate immunity, such as defensins and eosinophil-derived neurotoxin, were upregulated. Instead of employing clustering methods, we ranked the severity of recovering SARS patients by generalized associate plots (GAP) according to the expression profiles of 52 signature genes. Through this method, we discovered a smooth transition pattern of severity from normal controls to acute SARS patients. The rank of SARS severity was significantly correlated with the recovery period (in days) and with the clinical pulmonary infection score. CONCLUSION: The use of the GAP approach has proved useful in analyzing the complexity and continuity of biological systems. The severity rank derived from the global expression profile of significantly regulated genes in patients may be useful for further elucidating the pathophysiology of their disease

Statistical identification of gene association by CID in application of constructing ER regulatory network

<p>Abstract</p> <p>Background</p> <p>A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating <it>in silico </it>inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor α (ERα) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A).</p> <p>Results</p> <p>The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC), Student's <it>t</it>-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete variable (X), it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays.</p> <p>Conclusion</p> <p>CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the association predicted by CID are applicable to the construction of transcriptional regulatory networks. This study shows how information from different data sources and learning algorithms can be integrated to investigate whether relevant regulatory mechanisms identified in cell models can also be partially re-identified in clinical samples of breast cancers.</p> <p>Availability</p> <p>the implementation of CID in R codes can be freely downloaded from <url>http://homepage.ntu.edu.tw/~lyliu/BC/</url>.</p

Plasma Low-Density Lipoprotein Cholesterol Correlates With Heart Function in Individuals With Type 2 Diabetes Mellitus: A Cross-Sectional Study

Background: Heart failure is a frequent complication of type 2 diabetes mellitus (T2DM). Plasma cholesterol, particularly the proatherogenic low-density lipoprotein (LDL) cholesterol, impairs heart function by promoting atheroma formation and ventricular dysfunction. Considering the established effect of cholesterol on the cardiovascular system, we hypothesized that plasma LDL cholesterol may influence left ventricular function in individuals with T2DM.Methods: This cross-sectional study was conducted at a tertiary care hospital in Taiwan. Enrollment criteria were patients exceeding 21 years of age with T2DM who received antidiabetic and cholesterol-lowering medications. Candidates were excluded if they had heart failure, acute cardiovascular events, or familial hypercholesterolemia. Participants received blood sampling for plasma lipids after a 12-h fast, followed by transthoracic echocardiography in the cardiology clinic.Results: The study enrolled 118 participants who were divided into two groups according to their plasma LDL cholesterol levels. Demographic characteristics including age (69.7 vs. 66.9 years, P = 0.159), body mass index (26.2 vs. 25.9 kg/m2, P = 0.66), diabetes duration (5.4 vs. 5.1 years, P = 0.48), hemoglobin A1c (7.2 vs. 7.5%, P = 0.225), and systolic blood pressure (129 vs. 130 mm Hg, P = 0.735) were similar between these groups. Moreover, all participants received similar antihypertensive medications. Participants with lower plasma LDL cholesterol levels had better heart function, as measured by the left ventricular ejection fraction (LVEF), than patients with higher LDL cholesterol levels (58.0 vs. 50.5%, P = 0.022). Multivariate regression analysis also showed an inverse correlation between plasma LDL cholesterol and left ventricular function (β coefficient: −0.110, P = 0.024).Conclusion: This study observed an inverse correlation between plasma LDL cholesterol and heart function in individuals with T2DM. Patients with higher levels of plasma LDL cholesterol had worse left ventricular function. Therefore, plasma LDL cholesterol may be a modifiable risk factor of heart failure in diabetes, but prospective studies are necessary to confirm this finding

Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone

IntroductionKawasaki disease (KD) may increase the risk of myocardial infarction or sudden death. In children, delayed KD diagnosis and treatment can increase coronary lesions (CLs) incidence by 25% and mortality by approximately 1%. This study focuses on the use of deep learning algorithm-based KD detection from cardiac ultrasound images.MethodsSpecifically, object detection for the identification of coronary artery dilatation and brightness of left and right coronary artery is proposed and different AI algorithms were compared. In infants and young children, a dilated coronary artery is only 1-2 mm in diameter than a normal one, and its ultrasound images demonstrate a large amount of noise background-this can be a considerable challenge for image recognition. This study proposes a framework, named Scaled-YOLOv4-HarDNet, integrating the recent Scaled-YOLOv4 but with the CSPDarkNet backbone replaced by the CSPHarDNet framework.ResultsThe experimental result demonstrated that the mean average precision (mAP) of Scaled-YOLOv4-HarDNet was 72.63%, higher than that of Scaled YOLOv4 and YOLOv5 (70.05% and 69.79% respectively). In addition, it could detect small objects significantly better than Scaled-YOLOv4 and YOLOv5.ConclusionsScaled-YOLOv4-HarDNet may aid physicians in detecting KD and determining the treatment approach. Because relatively few artificial intelligence solutions about images for KD detection have been reported thus far, this paper is expected to make a substantial academic and clinical contribution