Inorganic nanoparticles are ideal precursors for engineering barcodes for rapidly detecting diseases. Despite advances in the chemical design of these barcodes, they have not advanced to clinical use because they lack sensitivity and are not cost-effective due to requirement of a large read-out system. Here we combined recent advances in quantum dot barcode technology with smartphones and isothermal amplification to engineer a simple and low-cost chip-based wireless multiplex diagnostic device. We characterized the analytical performance of this device and demonstrated that the device is capable of detecting down to 1000 viral genetic copies per milliliter, and this enabled the diagnosis of patients infected with HIV or hepatitis B. More importantly, the barcoding enabled us to detect multiple infectious pathogens simultaneously, in a single test, in less than 1 h. This multiplexing capability of the device enables the diagnosis of infections that are difficult to differentiate clinically due to common symptoms such as a fever or rash. The integration of quantum dot barcoding technology with a smartphone reader provides a capacity for global surveillance of infectious diseases and the potential to accelerate knowledge exchange transfer of emerging or exigent disease threats with healthcare and military organizations in real time.

Evolution of supramolecular chirality from self-assembly of achiral compounds and control over its handedness is closely related to the evolution of life and development of supramolecular materials with desired handedness. Here we report a system where the entire process of induction, control and locking of supramolecular chirality can be manipulated by light. Combination of triphenylamine and diacetylene moieties in the molecular structure allows photoinduced self-assembly of the molecule into helical aggregates in a chlorinated solvent by visible light and covalent fixation of the aggregate via photopolymerization by ultraviolet light, respectively. By using visible circularly polarized light, the supramolecular chirality of the resulting aggregates is selectively and reversibly controlled by its rotational direction, and the desired supramolecular chirality can be arrested by irradiation with ultraviolet circularly polarized light. This methodology opens a route to ward the formation of supramolecular chiral conducting nanostructures from the self-assembly of achiral molecules.

There has been a major focus on the clinical translation of emerging technologies for diagnosing patients with infectious diseases, cancer, heart disease, and diabetes. However, most developments still remain at the academic stage where researchers use spiked target molecules to demonstrate the utility of a technology and assess the analytical performance. This approach does not account for the biological complexities and variabilities of human patient samples. As a technology matures and potentially becomes clinically viable, one important intermediate step in the translation process is to conduct a full clinical validation of the technology using a large number of patient samples. Here, we present a full detailed clinical validation of Quantum Dot (QD) barcode technology for diagnosing patients infected with Hepatitis B Virus (HBV). We further demonstrate that the detection of multiple regions of the viral genome using multiplexed QD barcodes improved clinical sensitivity from 54.9-66.7% to 80.4-90.5%, and describe how to use QD barcodes for optimal clinical diagnosis of patients. The use of QDs in biology and medicine was first introduced in 1998 but has not reached clinical care. This study describes our long-term systematic development strategy to advance QD technology to a clinically feasible product for diagnosing patients. Our "blueprint" for translating the QD barcode research concept could be adapted for other nanotechnologies, to efficiently advance diagnostic techniques discovered in the academic laboratory to patient care.

An incremental stroke protocol that includes CT perfusion increases diagnostic performance for stroke diagnosis and inter- and intraobserver agreement.

We propose a novel memory network model named Read-Write Memory Network (RWMN) to perform question and answering tasks for large-scale, multimodal movie story understanding. The key focus of our RWMN model is to design the read network and the write network that consist of multiple convolutional layers, which enable memory read and write operations to have high capacity and flexibility. While existing memory-augmented network models treat each memory slot as an independent block, our use of multi-layered CNNs allows the model to read and write sequential memory cells as chunks, which is more reasonable to represent a sequential story because adjacent memory blocks often have strong correlations. For evaluation, we apply our model to all the six tasks of the MovieQA benchmark [26], and achieve the best accuracies on several tasks, especially on the visual QA task. Our model shows a potential to better understand not only the content in the story, but also more abstract information, such as relationships between characters and the reasons for their actions.