Metrics & MoreArticle Recommendations CONSPECTUS: Ferritins are spherical iron storage proteins within cells that are composed of a combination of 24 subunits of two types, heavy-chain ferritin (HFn) and light-chain ferritin (LFn). They autoassemble naturally into a spherical hollow nanocage with an outer diameter of 12 nm and an interior cavity that is 8 nm in diameter. In recent years, with the constantly emerging safety issues and the concerns about unfavorable uniformity and indefinite in vivo behavior of traditional nanomedicines, the characteristics of native ferritin nanocages, such as the unique nanocage structure, excellent safety profile, and definite in vivo behavior, make ferritin-based formulations uniquely attractive for nanomedicine development. To date, a variety of cargo molecules, including therapeutic drugs (e.g., cisplatin, carboplatin, paclitaxel, curcumin, atropine, quercetin, gefitinib, daunomycin, epirubicin, doxorubicin, etc.), imaging agents (e.g., fluorescence dyes, radioisotopes, and MRI contrast agents), nucleic acids (e.g., siRNA and miRNA), and metal nanoparticles (e.g., Fe 3 O 4 , CeO 2 , AuPd, CuS, CoPt, FeCo, Ag, etc.) have been loaded into the interior cavity of ferritin nanocages for a broad range of biomedical applications from in vitro biosensing to targeted delivery of cargo molecules in living systems with the aid of modified targeting ligands either genetically or chemically. We reported that human HFn could selectively deliver a large amount of cargo into tumors in vivo via transferrin receptor 1 (TfR1)-mediated tumor-cell-specific targeting followed by rapid internalization. By the use of the intrinsic tumor-targeting property and unique nanocage structure of human HFn, a broad variety of cargo-loaded HFn formulations have been developed for biological analysis, imaging diagnosis, and medicine development. In view of the intrinsic tumor-targeting property, unique nanocage structure, lack of immunogenicity, and definite in vivo behavior, human HFn holds promise to promote therapeutic drugs, diagnostic imaging agents, and targeting moieties into multifunctional nanomedicines.Since the report of the intrinsic tumor-targeting property of human HFn, we have extensively explored human HFn as an ideal nanocarrier for tumor-targeted delivery of anticancer drugs, MRI contrast agents, inorganic nanoparticles, and radioisotopes. In particular, by the use of genetic tools, we also have genetically engineered human HFn nanocages to recognize a broader range of disease biomarkers. In this Account, we systematically review human ferritins from characterizing their tumor-binding property and understanding their mechanism and kinetics for cargo loading to exploring their biomedical applications. We finally discuss the prospect of ferritin-based formulations to become next-generation nanomedicines. We expect that ferritin formulations with unique physicochemical characteristics and intrinsic tumor-targeting property will attract broad interest in fundamental drug research and offer new op...

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Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle/transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online.

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By moving a commercial 2D LiDAR, 3D maps of the environment can be built, based on the data of a 2D LiDAR and its movements. Compared to a commercial 3D LiDAR, a moving 2D LiDAR is more economical. A series of problems need to be solved in order for a moving 2D LiDAR to perform better, among them, improving accuracy and real-time performance. In order to solve these problems, estimating the movements of a 2D LiDAR, and identifying and removing moving objects in the environment, are issues that should be studied. More specifically, calibrating the installation error between the 2D LiDAR and the moving unit, the movement estimation of the moving unit, and identifying moving objects at low scanning frequencies, are involved. As actual applications are mostly dynamic, and in these applications, a moving 2D LiDAR moves between multiple moving objects, we believe that, for a moving 2D LiDAR, how to accurately construct 3D maps in dynamic environments will be an important future research topic. Moreover, how to deal with moving objects in a dynamic environment via a moving 2D LiDAR has not been solved by previous research.