Polarimetric Radar Convective Cell Tracking Reveals Large Sensitivity of Cloud Precipitation and Electrification Properties to CCN

Hypotheses have been proposed for decades about cloud condensation nuclei (CCN) aerosol effect on delaying the warm rain process, invigorating deep convective cloud vertical development, and enhancing mixed-phase process. Observational support has been only qualitative with mixed results due to lack of regional measurements of CCN, while simulations have not produced a robust consensus. Quantitative assessment of these relationships became possible with the advent of CCN retrievals from satellites; when combined with measurements by polarimetric radar and Lightning Mapping Array (LMA), tracking convective cells observed on radar and examining precipitation properties throughout the cells’ life cycle has permitted the study of the impact of CCNs on cloud and precipitation characteristics. We composited more than 2000 well-tracked cells in the Houston region and stratified them by CCN, convective available potential energy (CAPE) and urban/rural locations. The analyzed cell properties include reflectivity (Z), differential reflectivity (ZDR) and LMA data. The results show that added CCN to deep convective clouds delays the initiation of precipitation by up to 20 minutes. Added CCN invigorate the convection until saturation near CCN = 1000 cm^-3; increasing CCN from ~400 to an optimum of ~1000 cm^-3 increases lightning activity by an order of magnitude. A further increase of CCN decreases lightning rates. Adding CAPE enhances lightning only under low CCN <500 cm^-3. Urban area enhances lightning for the same CCN only under low CCN conditions. Urban heat island cannot explain this observation. In summary, CAPE is essential for the initiation of deep convection. It has been believed that CAPE and lightning are positively related. This is indeed the case when CAPE is low. But when CAPE is high, which means that deep convection is already in progress, aerosols dominate the lightning activity. These insights lead to refinement of the physical hypotheses which provide impetus for a field campaign in the Houston area

Shape Analysis Using Spectral Geometry

Shape analysis is a fundamental research topic in computer graphics and computer vision. To date, more and more 3D data is produced by those advanced acquisition capture devices, e.g., laser scanners, depth cameras, and CT/MRI scanners. The increasing data demands advanced analysis tools including shape matching, retrieval, deformation, etc. Nevertheless, 3D Shapes are represented with Euclidean transformations such as translation, scaling, and rotation and digital mesh representations are irregularly sampled. The shape can also deform non-linearly and the sampling may vary. In order to address these challenging problems, we investigate Laplace-Beltrami shape spectra from the differential geometry perspective, focusing more on the intrinsic properties. In this dissertation, the shapes are represented with 2 manifolds, which are differentiable. First, we discuss in detail about the salient geometric feature points in the Laplace-Beltrami spectral domain instead of traditional spatial domains. Simultaneously, the local shape descriptor of a feature point is the Laplace-Beltrami spectrum of the spatial region associated to the point, which are stable and distinctive. The salient spectral geometric features are invariant to spatial Euclidean transforms, isometric deformations and mesh triangulations. Both global and partial matching can be achieved with these salient feature points. Next, we introduce a novel method to analyze a set of poses, i.e., near-isometric deformations, of 3D models that are unregistered. Different shapes of poses are transformed from the 3D spatial domain to a geometry spectral one where all near isometric deformations, mesh triangulations and Euclidean transformations are filtered away. Semantic parts of that model are then determined based on the computed geometric properties of all the mapped vertices in the geometry spectral domain while semantic skeleton can be automatically built with joints detected. Finally we prove the shape spectrum is a continuous function to a scale function on the conformal factor of the manifold. The derivatives of the eigenvalues are analytically expressed with those of the scale function. The property applies to both continuous domain and discrete triangle meshes. On the triangle meshes, a spectrum alignment algorithm is developed. Given two closed triangle meshes, the eigenvalues can be aligned from one to the other and the eigenfunction distributions are aligned as well. This extends the shape spectra across non-isometric deformations, supporting a registration-free analysis of general motion data

Polarimetric Radar Convective Cell Tracking Reveals Large Sensitivity of Cloud Precipitation and Electrification Properties to CCN

Hypotheses have been proposed for decades about cloud condensation nuclei (CCN) aerosol effect on delaying the warm rain process, invigorating deep convective cloud vertical development, and enhancing mixed-phase process. Observational support has been only qualitative with mixed results due to lack of regional measurements of CCN, while simulations have not produced a robust consensus. Quantitative assessment of these relationships became possible with the advent of CCN retrievals from satellites; when combined with measurements by polarimetric radar and Lightning Mapping Array (LMA), tracking convective cells observed on radar and examining precipitation properties throughout the cells’ life cycle has permitted the study of the impact of CCNs on cloud and precipitation characteristics. We composited more than 2000 well-tracked cells in the Houston region and stratified them by CCN, convective available potential energy (CAPE) and urban/rural locations. The analyzed cell properties include reflectivity (Z), differential reflectivity (ZDR) and LMA data. The results show that added CCN to deep convective clouds delays the initiation of precipitation by up to 20 minutes. Added CCN invigorate the convection until saturation near CCN = 1000 cm^-3; increasing CCN from ~400 to an optimum of ~1000 cm^-3 increases lightning activity by an order of magnitude. A further increase of CCN decreases lightning rates. Adding CAPE enhances lightning only under low CCN <500 cm^-3. Urban area enhances lightning for the same CCN only under low CCN conditions. Urban heat island cannot explain this observation. In summary, CAPE is essential for the initiation of deep convection. It has been believed that CAPE and lightning are positively related. This is indeed the case when CAPE is low. But when CAPE is high, which means that deep convection is already in progress, aerosols dominate the lightning activity. These insights lead to refinement of the physical hypotheses which provide impetus for a field campaign in the Houston area

DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance

Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods try to extend pre-trained text-guided image diffusion models to image-guided video generation models. Nevertheless, these methods often result in either low fidelity or flickering over time due to their limitation to shallow image guidance and poor temporal consistency. To tackle these problems, we propose a high-fidelity image-to-video generation method by devising a frame retention branch based on a pre-trained video diffusion model, named DreamVideo. Instead of integrating the reference image into the diffusion process at a semantic level, our DreamVideo perceives the reference image via convolution layers and concatenates the features with the noisy latents as model input. By this means, the details of the reference image can be preserved to the greatest extent. In addition, by incorporating double-condition classifier-free guidance, a single image can be directed to videos of different actions by providing varying prompt texts. This has significant implications for controllable video generation and holds broad application prospects. We conduct comprehensive experiments on the public dataset, and both quantitative and qualitative results indicate that our method outperforms the state-of-the-art method. Especially for fidelity, our model has a powerful image retention ability and delivers the best results in UCF101 compared to other image-to-video models to our best knowledge. Also, precise control can be achieved by giving different text prompts. Further details and comprehensive results of our model will be presented in https://anonymous0769.github.io/DreamVideo/

Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease.

Medical imaging has been intensively employed in screening, diagnosis and monitoring during the COVID-19 pandemic. With the improvement of RT-PCR and rapid inspection technologies, the diagnostic references have shifted. Current recommendations tend to limit the application of medical imaging in the acute setting. Nevertheless, efficient and complementary values of medical imaging have been recognized at the beginning of the pandemic when facing unknown infectious diseases and a lack of sufficient diagnostic tools. Optimizing medical imaging for pandemics may still have encouraging implications for future public health, especially for long-lasting post-COVID-19 syndrome theranostics. A critical concern for the application of medical imaging is the increased radiation burden, particularly when medical imaging is used for screening and rapid containment purposes. Emerging artificial intelligence (AI) technology provides the opportunity to reduce the radiation burden while maintaining diagnostic quality. This review summarizes the current AI research on dose reduction for medical imaging, and the retrospective identification of their potential in COVID-19 may still have positive implications for future public health

Application of Benchtop NMR for Metabolomics Study Using Feces of Mice with DSS-Induced Colitis

Nuclear magnetic resonance (NMR)-based metabolomics, which comprehensively measures metabolites in biological systems and investigates their response to various perturbations, is widely used in research to identify biomarkers and investigate the pathogenesis of underlying diseases. However, further applications of high-field superconducting NMR for medical purposes and field research are restricted by its high cost and low accessibility. In this study, we applied a low-field, benchtop NMR spectrometer (60 MHz) employing a permanent magnet to characterize the alterations in the metabolic profile of fecal extracts obtained from dextran sodium sulfate (DSS)-induced ulcerative colitis model mice and compared them with the data acquired from high-field NMR (800 MHz). Nineteen metabolites were assigned to the 60 MHz 1H NMR spectra. Non-targeted multivariate analysis successfully discriminated the DSS-induced group from the healthy control group and showed high comparability with high-field NMR. In addition, the concentration of acetate, identified as a metabolite with characteristic behavior, could be accurately quantified using a generalized Lorentzian curve fitting method based on the 60 MHz NMR spectra.journal articl

Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction.

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanners of PET imaging. This study employs a simple way to integrate domain knowledge in DL for CT-free PET imaging. In contrast to conventional direct DL methods, we simplify the complex problem by a domain decomposition so that the learning of anatomy-dependent attenuation correction can be achieved robustly in a low-frequency domain while the original anatomy-independent high-frequency texture can be preserved during the processing. Even with the training from one tracer on one scanner, the effectiveness and robustness of our proposed approach are confirmed in tests of various external imaging tracers on different scanners. The robust, generalizable, and transparent DL development may enhance the potential of clinical translation

Association and dose–response relationship of plasma magnesium with metabolic syndrome in Chinese adults older than 45 years

PurposeMagnesium (Mg) is an essential nutrient for the maintenance of vital physiological functions. Magnesium deficiency is associated with diseases such as obesity, type 2 diabetes mellitus (T2DM), and metabolic syndrome (MetS); however, conclusions have been inconsistent, and there is a particular lack of evidence regarding this association in Chinese population older than 45 years. This study aimed to assess the association between plasma magnesium and the risk of MetS and its components, the dose–response relationship, and the threshold effect relationship in a Chinese population involving older than 45 years.MethodsA total of 2,101 individuals were randomly selected from the China Nutrition and Health Surveillance (CNHS) (2015–2017) by considering monitoring points. We used the joint statement of the International Diabetes Federation (IDF) in 2009 to define participants with MetS. The plasma magnesium was tested by inductively coupled plasma mass spectrometry (ICP-MS). The logistic regression and restricted cubic spline (RCS) models were used to analyze the association and dose–response relationship between plasma Mg and MetS and its components.ResultsCompared with the lowest quintile (Q1) for plasma Mg, the odds ratios (ORs) and 95% confidence intervals (95% CI) for MetS, impaired fasting glucose (IFG), hypertension, and triglyceride (TG) elevation at the highest quintile (Q5) were 0.419 (0.301, 0.583), 0.303 (0.221, 0.415), 0.446 (0.322, 0.618), and 0.526 (0.384, 0.720), respectively, with all p &lt; 0.05. However, in the components of decreased high-density lipoprotein cholesterol (HDL-C) and central obesity, no trend toward lowering with higher plasma magnesium was observed (p = 0.717, p = 0.865). These associations were not altered by further adjustment for potential confounding variables, including age, gender, education, nationality, area, residence, body mass index (BMI), and heart rate. The RCS analysis showed that, when plasma magnesium was lower than 0.85 mmol/L, the curve was leveled off, and then, the curve showed a decreasing trend with the increase in plasma magnesium.ConclusionTherefore, plasma Mg was negatively associated with MetS and its components (including IFG, hypertension, and elevated TG) in people older than 45 years. In addition, plasma Mg greater than or equal to 0.85 mmol/L, which is higher than the commonly used threshold of 0.75 mmol/L, may be protective against MetS and its components (including elevated FPG, elevated blood pressure, and elevated TG). More prospective studies, such as randomized controlled trials, are necessary to confirm the effective impact of Mg on MetS and its components. Plasma Mg levels in the MetS population older than 45 years require attention