The Purcell effect of silver nanoshell on the fluorescence of nanoparticles

Proceedings of the Asia Optical Fiber Communication and Optoelectronics Conference, 2007, p. 81-83The Purcell effect on the spontaneously emission rate and fluorescence efficiency of nanoparticles with and without a silver nanoshell will be investigated which are important for nanoparticle applications in biomedical diagnostics, information storage and optoelectronics.published_or_final_versio

Fast Deep Matting for Portrait Animation on Mobile Phone

Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose a real-time automatic deep matting approach for mobile devices. By leveraging the densely connected blocks and the dilated convolution, a light full convolutional network is designed to predict a coarse binary mask for portrait images. And a feathering block, which is edge-preserving and matting adaptive, is further developed to learn the guided filter and transform the binary mask into alpha matte. Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps. The experiments show that the proposed approach achieves comparable results with the state-of-the-art matting solvers.Comment: ACM Multimedia Conference (MM) 2017 camera-read

Learning-based Ensemble Average Propagator Estimation

By capturing the anisotropic water diffusion in tissue, diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the tissue microstructure and orientation in the human brain. The diffusion profile can be described by the ensemble average propagator (EAP), which is inferred from observed diffusion signals. However, accurate EAP estimation using the number of diffusion gradients that is clinically practical can be challenging. In this work, we propose a deep learning algorithm for EAP estimation, which is named learning-based ensemble average propagator estimation (LEAPE). The EAP is commonly represented by a basis and its associated coefficients, and here we choose the SHORE basis and design a deep network to estimate the coefficients. The network comprises two cascaded components. The first component is a multiple layer perceptron (MLP) that simultaneously predicts the unknown coefficients. However, typical training loss functions, such as mean squared errors, may not properly represent the geometry of the possibly non-Euclidean space of the coefficients, which in particular causes problems for the extraction of directional information from the EAP. Therefore, to regularize the training, in the second component we compute an auxiliary output of approximated fiber orientation (FO) errors with the aid of a second MLP that is trained separately. We performed experiments using dMRI data that resemble clinically achievable $q$-space sampling, and observed promising results compared with the conventional EAP estimation method.Comment: Accepted by MICCAI 201

Effects of different doses of melamine in the diet on melamine concentrations in milk, plasma, rumen fluid, urine and feces in lactating dairy cows

The objectives of this paper were to evaluate the effects of feeding diets containing different levels of melamine on melamine concentrations in milk, plasma, rumen fluid, urine and feces in Holstein dairy cows. Sixteen Chinese Holstein dairy cows fixed with permanent ruminal cannulas were assigned to 1 of 4 treatments within a completely randomized design for 10 days. Cows were fed different amounts of melamine {20 (group 1), 40 (group 2), 60 (group 3) or 80 (group 4) g/day/cow} once daily in the morning mixed with a melamine free basal diet for 7-days adaptation followed by 3-days urine and feces sample collections. Melamine was found in all samples tested and its concentration generally increased as dose increased in the diet. These results indicated that different doses of melamine in the diet could result in different concentrations of melamine in milk, plasma, rumen fluid, urine and feces. Data suggested that melamine primarily cleared by urinary excretion, followed by fecal excretion in lactating dairy cows. Mammary tissue was apparently not a major tissue to dispose melamine, especially when fed a relatively low dose (lower than 40 g/day/cow).Key words: Melamine, excretion, lactating dairy cow

Wavelength-multiplexed duplex transceiver based on III-V/Si hybrid integration for off-chip and on-chip optical interconnects

A six-channel wavelength-division-multiplexed optical transceiver with a compact footprint of 1.5 x 0.65 mm(2) for off-chip and on-chip interconnects is demonstrated on a single silicon-on-insulator chip. An arrayed waveguide grating is used as the (de)multiplexer, and III-V electroabsorption sections fabricated by hybrid integration technology are used as both modulators and detectors, which also enable duplex links. The 30-Gb/s capacity for each of the six wavelength channels for the off-chip transceiver is demonstrated. For the on-chip interconnect, an electrical-to-electrical 3-dB bandwidth of 13 GHz and a data rate of 30 Gb/s per wavelength are achieved

High-altitude population neonatal and maternal phenotypes associated with birthweight protection.

BACKGROUND: States which reduce foetal oxygen delivery are associated with impaired intrauterine growth. Hypoxia results when barometric pressure falls with ascent to altitude, and with it the partial pressure of inspired oxygen ('hypobaric hypoxia'). birthweight is reduced when native lowlanders gestate at such high altitude (HA)-an effect mitigated in native (millennia) HA populations. Studying HA populations offer a route to explore the mechanisms by which hypoxia impacts foetal growth. METHODS: Between February 2017 and January 2019, we prospectively studied 316 pregnant women, in Leh, Ladakh (altitude 3524 m, where oxygen partial pressure is reduced by 1/3) and 101 pregnant women living in Delhi (low altitude, 216 m above sea level). RESULTS: Of Ladakhi HA newborns, 14% were small for gestational age (10th weight centile. CONCLUSIONS: This study showed that Ladakhi offspring birthweight is relatively spared from the expected adverse HA effects. Furthermore, maternal body composition and greater UtA size may be physiological HA adaptations and warrant further study, as they offer potential mechanisms to overcome hypoxia-related growth issues. IMPACT: Reduced foetal oxygen delivery seen in native lowlanders who gestate at HA causes foetal growth restriction-an effect thought to be mitigated in native HA populations. We found that greater maternal body mass and UtA diameter were associated with increased offspring birthweight in a (Ladakh) HA population. This supports a role for them as physiological mediators of adaptation and provides insights into potential mechanisms that may treat hypoxia-related growth issues

Purification and characterization of chitinase from Alcaligenes faecalis AU02 by utilizing marine wastes and its antioxidant activity

Marine waste is an abundant renewable source for the recovery of several value added metabolites with potential industrial applications. This study describes the production of chitinase on marine waste, with the subsequent use of the same marine waste for the extraction of antioxidants. A chitinase-producing bacterium isolated from seafood effluent was identified as Alcaligenes faecalis AU02. Optimal chitinase production was obtained in culture conditions of 37°C for 72 h in 100 ml medium containing 1% shrimp and crab shell powder (1:1) (w/v), 0.1% K2HPO4, and 0.05% MgSO4·7H2O. The molecular weight of chitinase was determined by SDS-PAGE to be 36 kDa. The optimum pH, temperature, pH stability, and thermal stability of chitinase were about 8, 37°C, 5–12, and 40–80°C, respectively. The antioxidant activity of A. faecalis AU02 culture supernatant was determined through scavenging ability on 1,1-diphenyl-2-picrylhydrazyl (DPPH) as 84%, and the antioxidant compound was characterized by TLC and its FT-IR spectrum. The present study proposed that marine wastes can be utilized to generate a high-value-added product and that pharmacological studies can extend its use to the field of medicine

Non-universal minimal Z' models: present bounds and early LHC reach

We consider non-universal 'minimal' Z' models, whose additional U(1) charge is a non-anomalous linear combination of the weak hypercharge Y, the baryon number B and the partial lepton numbers (L_e, L_mu, L_tau), with no exotic fermions beyond three standard families with right-handed neutrinos. We show that the observed pattern of neutrino masses and mixing can be fully reproduced by a gauge-invariant renormalizable Lagrangian, and flavor-changing neutral currents in the charged lepton sector are suppressed by a GIM mechanism. We then discuss the phenomenology of some benchmark models. The electrophilic B-3L_e model is significantly constrained by electroweak precision tests, but still allows to fit the hint of an excess observed by CDF in dielectrons but not in dimuons. The muonphilic B-3L_mu model is very mildly constrained by electroweak precision tests, so that even the very early phase of the LHC can explore significant areas of parameter space. We also discuss the hadrophobic L_mu-L_tau model, which has recently attracted interest in connection with some puzzling features of cosmic ray spectra.Comment: 29 pages, 13 figure

Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention

Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for denoising, augmentation and balancing recording numbers of variant classes. In consideration of efficiency and consistency of data length, the recordings are padded or truncated into a medium length, where the padding/truncating time windows are selected randomly to sup-press overfitting. Then, the ECGs are used to train deep neural network (DNN) models with a novel structure that combines a deep residual network with an attention mechanism. Finally, an ensemble model is built based on these trained models to make predictions on the test data set. Our method is evaluated based on the test set of the First China ECG Intelligent Competition dataset by using the F1 metric that is regarded as the harmonic mean between the precision and recall. The resultant overall F1 score of the algorithm is 0.875, showing a promising performance and potential for practical use.Comment: 8 pages, 2 figures, conferenc

X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies

The morbidity of brain stroke increased rapidly in the past few years. To help specialists in lesion measurements and treatment planning, automatic segmentation methods are critically required for clinical practices. Recently, approaches based on deep learning and methods for contextual information extraction have served in many image segmentation tasks. However, their performances are limited due to the insufficient training of a large number of parameters, which sometimes fail in capturing long-range dependencies. To address these issues, we propose a depthwise separable convolution based X-Net that designs a nonlocal operation namely Feature Similarity Module (FSM) to capture long-range dependencies. The adopted depthwise convolution allows to reduce the network size, while the developed FSM provides a more effective, dense contextual information extraction and thus facilitates better segmentation. The effectiveness of X-Net was evaluated on an open dataset Anatomical Tracings of Lesions After Stroke (ATLAS) with superior performance achieved compared to other six state-of-the-art approaches. We make our code and models available at https://github.com/Andrewsher/X-Net.Comment: MICCAI 201