selenium, selenoproteins, ADHD, depression, COVID-19, oxidative stress

In the pharmaceutical industry, more and more nutritional supplements are entering the market, the composition of which contains the trace element selenium, which has scientifically proven benefits for the human body.It is important for the proper implementation of human processes as well as for the maintenance of human systems. It has been proven that with an insufficient intake of selenium, the development of chronic degenerative diseases is possible. The trace element is known for its protection against oxidative stress in the human body, thanks to selenoproteins that break down hydrogen peroxide.Over 5.9% of the world's population suffers from Attention Deficit Hyperactivity Disorder (ADHD), which is characterized by inattention, impulsivity, and hyperactivity. Millions of people suffer from depression or have experienced it at some point in their lives. It is a mood disorder characterized by feelings of inadequacy, despondency, decreased activity, pessimism, and sadness.The COVID-19 pandemic has affected millions of people worldwide and resulted in hundreds of thousands of deaths. Currently, much research is focused on supportive nutritional therapies that can mitigate the susceptibility as well as the long-term complications of COVID-19. Selenium plays a key role in strengthening immunity, preventing viral infections, and supporting therapy in critical illnesses. In addition, its deficiency can affect the severity of the disease.The conditions listed above can be influenced by selenium, thanks to selenoproteins and their influence on oxidative stress

Neural 3D Video Synthesis

We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation. Our approach takes the high quality and compactness of static neural radiance fields in a new direction: to a model-free, dynamic setting. At the core of our approach is a novel time-conditioned neural radiance fields that represents scene dynamics using a set of compact latent codes. To exploit the fact that changes between adjacent frames of a video are typically small and locally consistent, we propose two novel strategies for efficient training of our neural network: 1) An efficient hierarchical training scheme, and 2) an importance sampling strategy that selects the next rays for training based on the temporal variation of the input videos. In combination, these two strategies significantly boost the training speed, lead to fast convergence of the training process, and enable high quality results. Our learned representation is highly compact and able to represent a 10 second 30 FPS multi-view video recording by 18 cameras with a model size of just 28MB. We demonstrate that our method can render high-fidelity wide-angle novel views at over 1K resolution, even for highly complex and dynamic scenes. We perform an extensive qualitative and quantitative evaluation that shows that our approach outperforms the current state of the art. We include additional video and information at: https://neural-3d-video.github.io/Comment: Project website: https://neural-3d-video.github.io