A Simple Model for Cavity Enhanced Slow Lights in Vertical Cavity Surface Emission Lasers

We develop a simple model for the slow lights in Vertical Cavity Surface Emission Lasers (VCSELs), with the combination of cavity and population pulsation effects. The dependences of probe signal power, injection bias current and wavelength detuning for the group delays are demonstrated numerically and experimentally. Up to 65 ps group delays and up to 10 GHz modulation frequency can be achieved in the room temperature at the wavelength of 1.3 $\mu$m. The most significant feature of our VCSEL device is that the length of active region is only several $\mu$m long. Based on the experimental parameters of quantum dot VCSEL structures, we show that the resonance effect of laser cavity plays a significant role to enhance the group delays

Synthesis of Boron-Containing Primary Amines

[[abstract]]In this study, boron-containing primary amines were synthesized for use as building blocks in the study of peptoids. In the first step, Gabriel synthesis conditions were modified to enable the construction of seven different aminomethylphenyl boronate esters in good to excellent yields. These compounds were further utilized to build peptoid analogs via an Ugi four-component reaction (Ugi-4CR) under microwave irradiation. The prepared Ugi-4CR boronate esters were then successfully converted to the corresponding boronic acids. Finally, the peptoid structures were successfully modified by cross-coupling to aryl/heteroaryl chlorides via a palladium-mediated Suzuki coupling reaction to yield the corresponding derivatives in moderate to good yields.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙

Health Related Quality of Life in Clinical Studies for Chronic Diseases—Design and Analytical Considerations

The dissertation uses health related quality of life (HRQoL) for evaluating treatment effectiveness. The research settings in the dissertation include observational cohort study and randomized clinical trial. The first research project examines the effect of Sahaja Yoga Meditation on quality of life. The study investigates whether the post intervention HRQoL, perceived anxiety assessment and hypertensive control is different in patients treated with meditation plus conventional therapy than patients treated with conventional therapy alone. The study shows that Sahaja Yoga Meditation treatment is associated with significant improvements in quality of life, anxiety reduction and blood pressure control. The second research project examines the association of age on clinical and quality of life outcomes in the Bypass Angioplasty Revascularization Investigation 2 Diabetes Trial (BARI 2D); specifically, among patients with both type 2 diabetes and coronary heart disease, whether a treatment strategy (prompt revascularization or medical therapy for heart disease and insulin sensitizing or insulin providing drugs for type 2 diabetes) is more preferable for older individuals in terms of clinical and HRQoL endpoints. The study shows that older patients are at greater risk for cardiovascular events but the effectiveness of cardiac treatment strategies and glycemic control strategies does not differ by age. Older patients experience an accelerated decline in health status than their younger peers. The third research project investigates the longitudinal relationship between body mass index (BMI) and heath status outcomes in BARI 2D. The study reports an inverse association between BMI and health status outcomes in patients with both stable ischemic heart disease and type 2 diabetes. Weight reduction is desirable for obese patients, but may not be necessary for overweight but non-obese patients to achieve improvements in functional capacity and perceived Energy outcomes. The public health significance of the dissertation lies in the research findings on treatments that result in better clinical or quality of life outcomes for patients with chronic diseases, but also contrasts the strength and weakness of HRQoL studies and demonstrates strategies to overcome the methodological challenges in conducting HRQoL research in clinical studies

Adipose-Derived Mesenchymal Stem Cell Protects Kidneys against Ischemia-Reperfusion Injury through Suppressing Oxidative Stress and Inflammatory Reaction

Abstract Background Reactive oxygen species are important mediators exerting toxic effects on various organs during ischemia-reperfusion (IR) injury. We hypothesized that adipose-derived mesenchymal stem cells (ADMSCs) protect the kidney against oxidative stress and inflammatory stimuli in rat during renal IR injury. Methods Adult male Sprague-Dawley (SD) rats (n = 24) were equally randomized into group 1 (sham control), group 2 (IR plus culture medium only), and group 3 (IR plus immediate intra-renal administration of 1.0 × 106 autologous ADMSCs, followed by intravenous ADMSCs at 6 h and 24 h after IR). The duration of ischemia was 1 h, followed by 72 hours of reperfusion before the animals were sacrificed. Results Serum creatinine and blood urea nitrogen levels and the degree of histological abnormalities were markedly lower in group 3 than in group 2 (all p Conclusion ADMSC therapy minimized kidney damage after IR injury through suppressing oxidative stress and inflammatory response.</p

Electronic health record-wide association study for atrial fibrillation in a British cohort

Background: Atrial fibrillation (AF) confers a major healthcare burden from hospitalisations and AF-related complications, such as stroke and heart failure. We performed an electronic health records-wide association study to identify the most frequent reasons for healthcare utilization, pre and post new-onset AF. Methods: Prospective cohort study with the linked electronic health records of 5.6 million patients in the United Kingdom Clinical Practice Research Datalink (1998–2016). A cohort study with AF patients and their age-and sex matched controls was implemented to compare the top 100 reasons of frequent hospitalisation and primary consultation. Results: Of the 199,433 patients who developed AF, we found the most frequent healthcare interactions to be cardiac, cerebrovascular and peripheral-vascular conditions, both prior to AF diagnosis (41/100 conditions in secondary care, such as cerebral infarction and valve diseases; and 33/100 conditions in primary care), and subsequently (47/100 conditions hospital care and 48 conditions in primary care). There was a high representation of repeated visits for cancer and infection affecting multiple organ systems. We identified 10 novel conditions which have not yet been associated with AF: folic acid deficiency, pancytopenia, idiopathic thrombocytopenic purpura, seborrheic dermatitis, lymphoedema, angioedema, laryngopharyngeal reflux, rib fracture, haemorrhagic gastritis, inflammatory polyneuropathies. Conclusion: Our nationwide data provide knowledge and better understanding of the clinical needs of AF patients suggesting: (i) groups at higher risk of AF, where screening may be more cost-effective, and (ii) potential complications developing following new-onset AF that can be prevented through implementation of comprehensive integrated care management and more personalised, tailored treatment.</p

SASMU: boost the performance of generalized recognition model using synthetic face dataset

Nowadays, deploying a robust face recognition product becomes easy with the development of face recognition techniques for decades. Not only profile image verification but also the state-of-the-art method can handle the in-the-wild image almost perfectly. However, the concern of privacy issues raise rapidly since mainstream research results are powered by tons of web-crawled data, which faces the privacy invasion issue. The community tries to escape this predicament completely by training the face recognition model with synthetic data but faces severe domain gap issues, which still need to access real images and identity labels to fine-tune the model. In this paper, we propose SASMU, a simple, novel, and effective method for face recognition using a synthetic dataset. Our proposed method consists of spatial data augmentation (SA) and spectrum mixup (SMU). We first analyze the existing synthetic datasets for developing a face recognition system. Then, we reveal that heavy data augmentation is helpful for boosting performance when using synthetic data. By analyzing the previous frequency mixup studies, we proposed a novel method for domain generalization. Extensive experimental results have demonstrated the effectiveness of SASMU, achieving state-of-the-art performance on several common benchmarks, such as LFW, AgeDB-30, CA-LFW, CFP-FP, and CP-LFW.Comment: under revie