No abstract

We address the problem of synthesizing new video frames in an existing video, either in-between existing frames (interpolation), or subsequent to them (extrapolation). This problem is challenging because video appearance and motion can be highly complex. Traditional optical-flow-based solutions often fail where flow estimation is challenging, while newer neural-network-based methods that hallucinate pixel values directly often produce blurry results. We combine the advantages of these two methods by training a deep network that learns to synthesize video frames by flowing pixel values from existing ones, which we call deep voxel flow. Our method requires no human supervision, and any video can be used as training data by dropping, and then learning to predict, existing frames. The technique is efficient, and can be applied at any video resolution. We demonstrate that our method produces results that both quantitatively and qualitatively improve upon the state-ofthe-art.

Biological monitoring techniques are useful for risk assessment of toxic agents in the field of environmental health. Lead, a systemic toxicant affecting virtually every organ system, primarily affects the central nervous system, particularly the developing brain. Consequently, children are at a greater risk than adults of suffering from the neurotoxic effects of lead. The ability of lead to pass through the blood-brain barrier is due in large part to its ability to substitute for calcium ions. Within the brain, lead-induced damage in the prefrontal cerebral cortex, hippocampus, and cerebellum can lead to a variety of neurological disorders, such as brain damage, mental retardation, behavioral problems, nerve damage, and possibly Alzheimer’s disease, Parkinson’s disease, and schizophrenia. At the molecular level, lead interferes with the regulatory action of calcium on cell functions and disrupts many intracellular biological activities. Experimental studies have also shown that lead exposure may have genotoxic effects, especially in the brain, bone marrow, liver, and lung cells. This paper presents an overview of biomarkers of lead exposure and discusses the neurotoxic effects of lead with regard to children, adults, and experimental animals, updated to January 2009.

Human mesenchymal stem/stromal cells (hMSCs) have been shown to support breast cancer cell proliferation and metastasis, partly through their secretome. hMSCs have a remarkable ability to survive for long periods under stress, and their secretome is tumor supportive. In this study, we have characterized the cargo of extracellular vesicular (EV) fraction (that is in the size range of 40-150nm) of serum deprived hMSCs (SD-MSCs). Next Generation Sequencing assays were used to identify small RNA secreted in the EVs, which indicated presence of tumor supportive miRNA. Further assays demonstrated the role of miRNA-21 and 34a as tumor supportive miRNAs. Next, proteomic assays revealed the presence of ≈150 different proteins, most of which are known tumor supportive factors such as PDGFR-β, TIMP-1, and TIMP-2. Lipidomic assays verified presence of bioactive lipids such as sphingomyelin. Furthermore, metabolite assays identified the presence of lactic acid and glutamic acid in EVs. The co-injection xenograft assays using MCF-7 breast cancer cells demonstrated the tumor supportive function of these EVs. To our knowledge this is the first comprehensive -omics based study that characterized the complex cargo of extracellular vesicles secreted by hMSCs and their role in supporting breast cancers.

Image editing applications offer a wide array of tools for color manipulation. Some of these tools are easy to understand but offer a limited range of expressiveness. Other more powerful tools are time consuming for experts and inscrutable to novices. Researchers have described a variety of more sophisticated methods but these are typically not interactive, which is crucial for creative exploration. This paper introduces a simple, intuitive and interactive tool that allows non-experts to recolor an image by editing a color palette. This system is comprised of several components: a GUI that is easy to learn and understand, an efficient algorithm for creating a color palette from an image, and a novel color transfer algorithm that recolors the image based on a user-modified palette. We evaluate our approach via a user study, showing that it is faster and easier to use than two alternatives, and allows untrained users to achieve results comparable to those of experts using professional software.