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"Chan, Jonathan"
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Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images
Spaceborne hyperspectral images are useful for large scale mineral mapping. Acquired at a ground sampling distance (GSD) of 30 m, the Environmental Mapping and Analysis Program (EnMAP) will be capable of putting many issues related to environment monitoring and resource exploration in perspective with measurements in the spectral range between 420 and 2450 nm. However, a higher spatial resolution is preferable for many applications. This paper investigates the potential of fusion-based resolution enhancement of hyperspectral data for mineral mapping. A pair of EnMAP and Sentinel-2 images is generated from a HyMap scene over a mining area. The simulation is based on well-established sensor end-to-end simulation tools. The EnMAP image is fused with Sentinel-2 10-m-GSD bands using a matrix factorization method to obtain resolution-enhanced EnMAP data at a 10 m GSD. Quality assessments of the enhanced data are conducted using quantitative measures and continuum removal and both show that high spectral and spatial fidelity are maintained. Finally, the results of spectral unmixing are compared with those expected from high-resolution hyperspectral data at a 10 m GSD. The comparison demonstrates high resemblance and shows the great potential of the resolution enhancement method for EnMAP type data in mineral mapping.
Journal Article
SGLT2 Inhibitors: The Next Blockbuster Multifaceted Drug?
Sodium glucose cotransporter 2 inhibitor (SGLT2i) is a class of drugs that were originally intended for decreasing blood glucose in diabetes. However, recent trials have shown that there are other beneficial effects. Major clinical trials involving SGLT2i medications from 2015 to 2022 were reviewed using PUBMED search. Recent major SGLT2i landmark trials have demonstrated benefits for cardiovascular disease (reduce major adverse cardiovascular events (heart attack, stroke, cardiovascular death), hospitalization for heart failure, all-cause death), and renal disease (delay the onset of dialysis) regardless of diabetic status. The consistent cardiorenal benefits observed in major landmark trials have resulted in the rapid adoption of SGLT2i therapy not only in diabetes guidelines but also cardiovascular and renal guidelines.
Journal Article
Hyperspectral and Multispectral Image Fusion via Deep Two-Branches Convolutional Neural Network
Enhancing the spatial resolution of hyperspectral image (HSI) is of significance for applications. Fusing HSI with a high resolution (HR) multispectral image (MSI) is an important technology for HSI enhancement. Inspired by the success of deep learning in image enhancement, in this paper, we propose a HSI-MSI fusion method by designing a deep convolutional neural network (CNN) with two branches which are devoted to features of HSI and MSI. In order to exploit spectral correlation and fuse the MSI, we extract the features from the spectrum of each pixel in low resolution HSI, and its corresponding spatial neighborhood in MSI, with the two CNN branches. The extracted features are then concatenated and fed to fully connected (FC) layers, where the information of HSI and MSI could be fully fused. The output of the FC layers is the spectrum of the expected HR HSI. In the experiment, we evaluate the proposed method on Airborne Visible Infrared Imaging Spectrometer (AVIRIS), and Environmental Mapping and Analysis Program (EnMAP) data. We also apply it to real Hyperion-Sentinel data fusion. The results on the simulated and the real data demonstrate that the proposed method is competitive with other state-of-the-art fusion methods.
Journal Article
Hyperspectral Images Classification Based on Dense Convolutional Networks with Spectral-Wise Attention Mechanism
Hyperspectral images (HSIs) data that is typically presented in 3-D format offers an opportunity for 3-D networks to extract spectral and spatial features simultaneously. In this paper, we propose a novel end-to-end 3-D dense convolutional network with spectral-wise attention mechanism (MSDN-SA) for HSI classification. The proposed MSDN-SA exploits 3-D dilated convolutions to simultaneously capture the spectral and spatial features at different scales, and densely connects all 3-D feature maps with each other. In addition, a spectral-wise attention mechanism is introduced to enhance the distinguishability of spectral features, which improves the classification performance of the trained models. Experimental results on three HSI datasets demonstrate that our MSDN-SA achieves competitive performance for HSI classification.
Journal Article
Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection
This paper studies the classification problem of hyperspectral image (HSI). Inspired by the great success of deep neural networks in Artificial Intelligence (AI), researchers have proposed different deep learning based algorithms to improve the performance of hyperspectral classification. However, deep learning based algorithms always require a large-scale annotated dataset to provide sufficient training. To address this problem, we propose a semi-supervised deep learning framework based on the residual networks (ResNets), which use very limited labeled data supplemented by abundant unlabeled data. The core of our framework is a novel dual-strategy sample selection co-training algorithm, which can successfully guide ResNets to learn from the unlabeled data by making full use of the complementary cues of the spectral and spatial features in HSI classification. Experiments on the benchmark HSI dataset and real HSI dataset demonstrate that, with a small number of training data, our approach achieves competitive performance with maximum improvement of 41% (compare with traditional convolutional neural network (CNN) with 5 initial training samples per class on Indian Pines dataset) for HSI classification as compared with the results from those state-of-the-art supervised and semi-supervised methods.
Journal Article
A noncanonical inhibitory circuit dampens behavioral sensitivity to light
Retinal ganglion cells (RGCs) drive diverse, light-evoked behaviors that range from conscious visual perception to subconscious, non–image-forming behaviors. It is thought that RGCs primarily drive these functions through the release of the excitatory neurotransmitter glutamate. We identified a subset of melanopsin-expressing intrinsically photosensitive RGCs (ipRGCs) in mice that release the inhibitory neurotransmitter γ-aminobutyric acid (GABA) at non–image-forming brain targets. GABA release from ipRGCs dampened the sensitivity of both the pupillary light reflex and circadian photoentrainment, thereby shifting the dynamic range of these behaviors to higher light levels. Our results identify an inhibitory RGC population in the retina and provide a circuit-level mechanism that contributes to the relative insensitivity of non–image-forming behaviors at low light levels.
Journal Article
Practical Pharmacological Treatment of Heart Failure: Does Ejection Fraction Matter Anymore?
Heart failure (HF) is a complex clinical syndrome involving structural and/or functional abnormalities of the heart. Heart failure is often classified based on left ventricular ejection fraction, which serves as a predictor of mortality. The majority of the data supporting disease-modifying pharmacological therapies are from patients with reduced ejection fraction (less than 40%). However, with the recent results from the sodium glucose cotransporter-2 inhibitor trials, there is renewed interest in identifying potential beneficial pharmacological therapies. This review focuses on and includes pharmacological HF therapies across the spectrum of ejection fraction, providing an overview of the novel trials. We also examined the effects of the treatments on mortality, hospitalization, functional status, and biomarker levels to further investigate the interplay between ejection fraction and HF.
Journal Article
Open access resource for cellular-resolution analyses of corticocortical connectivity in the marmoset monkey
Understanding the principles of neuronal connectivity requires tools for efficient quantification and visualization of large datasets. The primate cortex is particularly challenging due to its complex mosaic of areas, which in many cases lack clear boundaries. Here, we introduce a resource that allows exploration of results of 143 retrograde tracer injections in the marmoset neocortex. Data obtained in different animals are registered to a common stereotaxic space using an algorithm guided by expert delineation of histological borders, allowing accurate assignment of connections to areas despite interindividual variability. The resource incorporates tools for analyses relative to cytoarchitectural areas, including statistical properties such as the fraction of labeled neurons and the percentage of supragranular neurons. It also provides purely spatial (parcellation-free) data, based on the stereotaxic coordinates of 2 million labeled neurons. This resource helps bridge the gap between high-density cellular connectivity studies in rodents and imaging-based analyses of human brains.
Understanding principles of neuronal connectivity requires tools for quantification and visualization of large datasets. Here, the authors introduce an online resource encompassing the coordinates of two million neurons labelled by tracer injections in the marmoset cortex, and analysis tools.
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