Cardiovascular magnetic resonance reference ranges for the heart and aorta in Chinese at 3T.

Cardiovascular magnetic resonance (CMR) reference ranges have not been well established in Chinese. Here we determined normal cardiac and aortic reference ranges in healthy Singaporean Chinese and investigated how these data might affect clinical interpretation of CMR scans.In 180 healthy Singaporean Chinese (20 to 69 years old; males, n = 91), comprehensive cardiac assessment was performed using the steady state free precision technique (3T Ingenia, Philips) and images were analysed by two independent observers (CMR42, Circle Cardiovascular Imaging). Measurements were internally validated using standardized approaches: left ventricular mass (LVM) was measured in diastole and systole (with and without papillary muscles) and stroke volumes were compared in both ventricles. All reference ranges were stratified by sex and age; and indeterminate/borderline regions were defined statistically at the limits of the normal reference ranges. Results were compared with clinical measurements reported in the same individuals.LVM was equivalent in both phases (mean difference 3.0 ± 2.5 g; P = 0.22) and stroke volumes were not significantly different in the left and right ventricles (P = 0.91). Compared to females, males had larger left and right ventricular volumes (P  0.05 for all measures). In both sexes, age correlated negatively with left and right ventricular volumes; and positively with aortic sinus and sinotubular junction diameters (P < 0.0001 for all). There was excellent agreement in indexed stroke volumes in the left and right ventricles (0.1±5.7 mL/m2, 0.7±6.2 mL/m2, respectively), LVM (0.6±6.4 g/m2), atrial sizes and aortic root dimensions between values reported in clinical reports and our measured reference ranges.Comprehensive sex and age-corrected CMR reference ranges at 3T have been established in Singaporean Chinese. This is an important step for clinical practice and research studies of the heart and aorta in Asia

Towards green energy for smart cities: particle swarm optimization based MPPT approach

This paper proposes an improved one-power-point (OPP) maximum power point tracking (MPPT) algorithm for wind energy conversion system (WECS) to overcome the problems of the conventional OPP MPPT algorithm, namely, the difficulty in getting a precise value of the optimum coefficient, requiring pre-knowledge of system parameters, and non-uniqueness of the optimum curve. The solution is based on combining the particle swarm optimization (PSO) and optimum-relation-based (ORB) MPPT algorithms. The PSO MPPT algorithm is used to search for the optimum coefficient. Once the optimum coefficient is obtained, the proposed algorithm switches to the ORB MPPT mode of operation. The proposed algorithm neither requires knowledge of system parameters nor mechanical sensors. In addition, it improves the efficiency of the WECS. The proposed algorithm is studied for two different wind speed profiles, and its tracking performance is compared with conventional optimum torque control (OTC) and conventional ORB MPPT algorithms under identical conditions. The improved performance of the algorithm in terms of tracking efficiency is validated through simulation using MATLAB/Simulink. The simulation results confirm that the proposed algorithm has a better performance in terms of tracking efficiency and energy extracted. The tracking efficiency of the PSO-ORB MPPT algorithm could reach up to 99.4% with 1.9% more harvested electrical energy than the conventional OTC and ORB MPPT algorithms. Experiments have been carried out to demonstrate the validity of the proposed MPPT algorithm. The experimental results compare well with system simulation results, and the proposed algorithm performs well, as expected

A man with hypophosphataemia

Case report; A section on BMJ, 2011, v. 342, p. 715published_or_final_versio

Chinese herbal medicine for infertility with anovulation: a systematic review.

published_or_final_versio

Age-related changes in Serum Growth Hormone, Insulin-like Growth Factor-1 and Somatostatin in System Lupus Erythematosus

BACKGROUND: Systemic lupus erythematosus is an age- and gender-associated autoimmune disorder. Previous studies suggested that defects in the hypothalamic/pituitary axis contributed to systemic lupus erythematosus disease progression which could also involve growth hormone, insulin-like growth factor-1 and somatostatin function. This study was designed to compare basal serum growth hormone, insulin-like growth factor-1 and somatostatin levels in female systemic lupus erythematosus patients to a group of normal female subjects. METHODS: Basal serum growth hormone, insulin-like growth factor-1 and somatostatin levels were measured by standard radioimmunoassay. RESULTS: Serum growth hormone levels failed to correlate with age (r(2 )= 3.03) in the entire group of normal subjects (i.e. 20 – 80 years). In contrast, serum insulin-like growth factor-1 levels were inversely correlated with age (adjusted r(2 )= 0.092). Of note, serum growth hormone was positively correlated with age (adjusted r(2 )= 0.269) in the 20 – 46 year range which overlapped with the age range of patients in the systemic lupus erythematosus group. In that regard, serum growth hormone levels were not significantly higher compared to either the entire group of normal subjects (20 – 80 yrs) or to normal subjects age-matched to the systemic lupus erythematosus patients. Serum insulin-like growth factor-1 levels were significantly elevated (p < 0.001) in systemic lupus erythematosus patients, but only when compared to the entire group of normal subjects. Serum somatostatin levels differed from normal subjects only in older (i.e. >55 yrs) systemic lupus erythematosus patients. CONCLUSIONS: These results indicated that systemic lupus erythematosus was not characterized by a modulation of the growth hormone/insulin-like growth factor-1 paracrine axis when serum samples from systemic lupus erythematosus patients were compared to age- matched normal female subjects. These results in systemic lupus erythematosus differ from those previously reported in other musculoskeletal disorders such as rheumatoid arthritis, osteoarthritis, fibromyalgia, diffuse idiopathic skeletal hyperostosis and hypermobility syndrome where significantly higher serum growth hormone levels were found. Somatostatin levels in elderly systemic lupus erythematosus patients may provide a clinical marker of disease activity in these patients

Viscoelastic behaviour of human mesenchymal stem cells

<p>Abstract</p> <p>Background</p> <p>In this study, we have investigated the viscoelastic behaviour of individual human adult bone marrow-derived mesenchymal stem cells (hMSCs) and the role of F-actin filaments in maintaining these properties, using micropipette aspiration technique together with a standard linear viscoelastic solid model.</p> <p>Results</p> <p>Under a room temperature of 20°C, the instantaneous and equilibrium Young's modulus, <it>E</it>₀and <it>E</it>_∞, were found to be 886 ± 289 Pa and 372 ± 125 Pa, respectively, while the apparent viscosity, <it>μ</it>, was 2710 ± 1630 Pa·s. hMSCs treated with cytochalasin D up to 20 μM at 20°C registered significant drop of up to 84% in stiffness and increase of up to 255% in viscosity. At the physiological temperature of 37°C, <it>E</it>₀and <it>E</it>_∞have decreased by 42–66% whereas <it>μ </it>has increased by 95%, compared to the control. Majority of the hMSCs behave as viscoelastic solid with a rapid initial increase in aspiration length and it gradually levels out with time. Three other types of non-typical viscoelastic behavior of hMSCs were also seen.</p> <p>Conclusion</p> <p>hMSCs behave as viscoelastic solid. Its viscoelstic behaviour are dependent on the structural integrity of the F-actin filaments and temperature.</p

Harnessing technology and molecular analysis to understand the development of cardiovascular diseases in Asia: a prospective cohort study (SingHEART)

BACKGROUND: Cardiovascular disease (CVD) imposes much mortality and morbidity worldwide. The use of "deep learning", advancements in genomics, metabolomics, proteomics and devices like wearables have the potential to unearth new insights in the field of cardiology. Currently, in Asia, there are no studies that combine the use of conventional clinical information with these advanced technologies. We aim to harness these new technologies to understand the development of cardiovascular disease in Asia. METHODS: Singapore is a multi-ethnic country in Asia with well-represented diverse ethnicities including Chinese, Malays and Indians. The SingHEART study is the first technology driven multi-ethnic prospective population-based study of healthy Asians. Healthy male and female subjects aged 21-69 years old without any prior cardiovascular disease or diabetes mellitus will be recruited from the general population. All subjects are consented to undergo a detailed on-line questionnaire, basic blood investigations, resting and continuous electrocardiogram and blood pressure monitoring, activity and sleep tracking, calcium score, cardiac magnetic resonance imaging, whole genome sequencing and lipidomic analysis. Outcomes studied will include mortality and cause of mortality, myocardial infarction, stroke, malignancy, heart failure, and the development of co-morbidities. DISCUSSION: An initial target of 2500 patients has been set. From October 2015 to May 2017, an initial 683 subjects have been recruited and have completed the initial work-up the SingHEART project is the first contemporary population-based study in Asia that will include whole genome sequencing and deep phenotyping: including advanced imaging and wearable data, to better understand the development of cardiovascular disease across different ethnic groups in Asia

Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework

Recently, data-driven models such as deep neural networks have shown to be promising tools for modelling and state inference in soft robots. However, voluminous amounts of data are necessary for deep models to perform effectively, which requires exhaustive and quality data collection, particularly of state labels. Consequently, obtaining labelled state data for soft robotic systems is challenged for various reasons, including difficulty in the sensorization of soft robots and the inconvenience of collecting data in unstructured environments. To address this challenge, in this paper, we propose a semi-supervised sequential variational Bayes (DSVB) framework for transfer learning and state inference in soft robots with missing state labels on certain robot configurations. Considering that soft robots may exhibit distinct dynamics under different robot configurations, a feature space transfer strategy is also incorporated to promote the adaptation of latent features across multiple configurations. Unlike existing transfer learning approaches, our proposed DSVB employs a recurrent neural network to model the nonlinear dynamics and temporal coherence in soft robot data. The proposed framework is validated on multiple setup configurations of a pneumatic-based soft robot finger. Experimental results on four transfer scenarios demonstrate that DSVB performs effective transfer learning and accurate state inference amidst missing state labels. The data and code are available at https://github.com/shageenderan/DSVB.Comment: Accepted at the International Conference on Robotics and Automation (ICRA) 202

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Mapping photonic entanglement into and out of a quantum memory

Recent developments of quantum information science critically rely on entanglement, an intriguing aspect of quantum mechanics where parts of a composite system can exhibit correlations stronger than any classical counterpart. In particular, scalable quantum networks require capabilities to create, store, and distribute entanglement among distant matter nodes via photonic channels. Atomic ensembles can play the role of such nodes. So far, in the photon counting regime, heralded entanglement between atomic ensembles has been successfully demonstrated via probabilistic protocols. However, an inherent drawback of this approach is the compromise between the amount of entanglement and its preparation probability, leading intrinsically to low count rate for high entanglement. Here we report a protocol where entanglement between two atomic ensembles is created by coherent mapping of an entangled state of light. By splitting a single-photon and subsequent state transfer, we separate the generation of entanglement and its storage. After a programmable delay, the stored entanglement is mapped back into photonic modes with overall efficiency of 17 %. Improvements of single-photon sources together with our protocol will enable "on demand" entanglement of atomic ensembles, a powerful resource for quantum networking.Comment: 7 pages, and 3 figure