Life fingerprints of nuclear reactions in the body of animals

Nuclear reactions are a very important natural phenomenon in the universe. On the earth, cosmic rays constantly cause nuclear reactions. High energy beams created by medical devices also induce nuclear reactions in the human body. The biological role of these nuclear reactions is unknown. Here we show that the in vivo biological systems are exquisite and sophisticated by nature in influence on nuclear reactions and in resistance to radical damage in the body of live animals. In this study, photonuclear reactions in the body of live or dead animals were induced with 50-MeV irradiation. Tissue nuclear reactions were detected by positron emission tomography (PET) imaging of the induced beta+ activity. We found the unique tissue "fingerprints" of beta+ (the tremendous difference in beta+ activities and tissue distribution patterns among the individuals) are imprinted in all live animals. Within any individual, the tissue "fingerprints" of 15O and 11C are also very different. When the animal dies, the tissue "fingerprints" are lost. The biochemical, rather than physical, mechanisms could play a critical role in the phenomenon of tissue "fingerprints". Radiolytic radical attack caused millions-fold increases in 15O and 11C activities via different biochemical mechanisms, i.e. radical-mediated hydroxylation and peroxidation respectively, and more importantly the bio-molecular functions (such as the chemical reactivity and the solvent accessibility to radicals). In practice biologically for example, radical attack can therefore be imaged in vivo in live animals and humans using PET for life science research, disease prevention, and personalized radiation therapy based on an individual's bio-molecular response to ionizing radiation

EUCLIA - Exploring the UV/optical continuum lag in active galactic nuclei. I. a model without light echoing

The tight inter-band correlation and the lag-wavelength relation among UV/optical continua of active galactic nuclei have been firmly established. They are usually understood within the widespread reprocessing scenario, however, the implied inter-band lags are generally too small. Furthermore, it is challenged by new evidences, such as the X-ray reprocessing yields too much high frequency UV/optical variations as well as it fails to reproduce the observed timescale-dependent color variations among {\it Swift} lightcurves of NGC 5548. In a different manner, we demonstrate that an upgraded inhomogeneous accretion disk model, whose local {\it independent} temperature fluctuations are subject to a speculated {\it common} large-scale temperature fluctuation, can intrinsically generate the tight inter-band correlation and lag across UV/optical, and be in nice agreement with several observational properties of NGC 5548, including the timescale-dependent color variation. The emergent lag is a result of the {\it differential regression capability} of local temperature fluctuations when responding to the large-scale fluctuation. An average speed of propagations as large as $\gtrsim 15\%$ of the speed of light may be required by this common fluctuation. Several potential physical mechanisms for such propagations are discussed. Our interesting phenomenological scenario may shed new light on comprehending the UV/optical continuum variations of active galactic nuclei.Comment: 18 pages, 8 figures. ApJ accepted. Further comments are very welcome

Learning Dense UV Completion for Human Mesh Recovery

Human mesh reconstruction from a single image is challenging in the presence of occlusion, which can be caused by self, objects, or other humans. Existing methods either fail to separate human features accurately or lack proper supervision for feature completion. In this paper, we propose Dense Inpainting Human Mesh Recovery (DIMR), a two-stage method that leverages dense correspondence maps to handle occlusion. Our method utilizes a dense correspondence map to separate visible human features and completes human features on a structured UV map dense human with an attention-based feature completion module. We also design a feature inpainting training procedure that guides the network to learn from unoccluded features. We evaluate our method on several datasets and demonstrate its superior performance under heavily occluded scenarios compared to other methods. Extensive experiments show that our method obviously outperforms prior SOTA methods on heavily occluded images and achieves comparable results on the standard benchmarks (3DPW)