Smart Materials for Wearable Healthcare Devices

Wearable devices seem to have great potential that could result in a revolutionary non-clinical approach to health monitoring and diagnosing disease. With continued innovation and intensive attention to the materials and fabrication technologies, development of these healthcare devices is progressively encouraged. This chapter gives a concise review of some of the main concepts and approaches related to recent advances and developments in the scope of wearable devices from the perspective of emerging materials. A complementary section of the review linking these advanced materials with wearable device technologies is particularly specified. Some of the strong and weak points in development of each wearable material/device are clearly highlighted and criticized

Crust: Verifiable And Efficient Private Information Retrieval with Sublinear Online Time

Private Information Retrieval (PIR) is a cryptographic primitive that enables a user to retrieve information from a database without revealing the particular information they are seeking, thus preserving their privacy. PIR schemes suffer from high computation overhead. By running an offline preprocessing phase, PIR schemes can achieve sublinear online server computation. On the other hand, although protocols for honest-but-curious servers have been well-studied in both single-server and multi-server scenarios, little work has been done for the case where the server is malicious. In this paper, we propose a simple but efficient sublinear PIR scheme named Crust. The scheme is tailored for verifiability and provides privacy and data integrity against malicious servers. Our scheme can work with two servers or a single server. Aside from verifiability, our scheme is very efficient. Compared to state-of-the-art two-server and single-server sublinear PIR schemes, our scheme is 22x more efficient in online computation. To the best of our knowledge, this is the first PIR scheme that achieves verifiability, as well as amortized $O(\sqrt{n})$ server computation

Gas Sensing by Microwave Transduction: Review of Progress and Challenges

Microwave transduction is a novel research field in gas sensing owing to its simplicity, low cost, passive, and non-contact operations that indicate a huge potential in applications for gas sensing. At present, the study of microwave gas sensors is limited to testing the macroscopic performance of materials. Although the permittivity change of materials is the reason for microwave transduction, the knowledge of the fundamental mechanism is an urgent requirement for boosting the development of microwave gas sensors. In this review, we summarized the presented progresses of microwave gas sensors, including the performance of the different materials and propagative structures, as well as their sensitive signal. We have attempted to explain the sensitive mechanism of these materials, discuss the function of the propagative structure, and analyze the sensitive signal extracted from the parameters of electromagnetic waves. Finally, the challenges in the study of sensitive materials and propagative structures used in microwave gas sensors are analyzed. It is clear from the published literatures that microwave transduction provide a new route for gas detection, and it is expected that commercial manifestation will occur. The future research on microwave gas sensors will continue to exploit their sensitive materials and propagative structure for different applications. The understanding of the microscopic polarization interactions with gases will assist in and guide the fabrication of high-performance microwave gas sensors in the future

Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients. However, current prognostic prediction methods fail to explicitly and accurately investigate relationships between the tumor and nearby important vessels. This paper proposes a novel learnable neural distance that describes the precise relationship between the tumor and vessels in CT images of different patients, adopting it as a major feature for prognosis prediction. Besides, different from existing models that used CNNs or LSTMs to exploit tumor enhancement patterns on dynamic contrast-enhanced CT imaging, we improved the extraction of dynamic tumor-related texture features in multi-phase contrast-enhanced CT by fusing local and global features using CNN and transformer modules, further enhancing the features extracted across multi-phase CT images. We extensively evaluated and compared the proposed method with existing methods in the multi-center (n=4) dataset with 1,070 patients with PDAC, and statistical analysis confirmed its clinical effectiveness in the external test set consisting of three centers. The developed risk marker was the strongest predictor of overall survival among preoperative factors and it has the potential to be combined with established clinical factors to select patients at higher risk who might benefit from neoadjuvant therapy.Comment: MICCAI 202

CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans

Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI's clinical adoption. A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan. In this paper, we construct a Unified Tumor Transformer (CancerUniT) model to jointly detect tumor existence & location and diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction. We decouple the object queries into organ queries, tumor detection queries and tumor diagnosis queries, and further establish hierarchical relationships among the three groups. This clinically-inspired architecture effectively assists inter- and intra-organ representation learning of tumors and facilitates the resolution of these complex, anatomically related multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using a curated large-scale CT images of 10,042 patients including eight major types of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D tumor masks annotated by radiologists). On the test set of 631 patients, CancerUniT has demonstrated strong performance under a set of clinically relevant evaluation metrics, substantially outperforming both multi-disease methods and an assembly of eight single-organ expert models in tumor detection, segmentation, and diagnosis. This moves one step closer towards a universal high performance cancer screening tool.Comment: ICCV 2023 Camera Ready Versio

Polydopamine nanoparticles for treatment of acute inflammation-induced injury

Nanotechnology-mediated anti-inflammatory therapy is emerging as a novel strategy for treatment of inflammation-induced injury. However, one of the main hurdles for these anti-inflammatory nano-drugs is their potential toxic side effects in vivo. Herein, we uncovered that polydopamine (PDA) nanoparticles with structure and chemical properties similar to melanin, a natural bio-polymer, displayed significant anti-inflammation therapeutic effect on acute inflammation-induced injury. PDA with enriched phenol groups functioned as a radical scavenger to eliminate reactive oxygen species (ROS) generated during inflammatory responses. As revealed by in vivo photoacoustic imaging with a H2O2-specific nanoprobe, PDA nanoparticles remarkably reduced intracellular ROS levels in murine macrophages challenged with either H2O2 or lipopolysaccharide (LPS). The anti-inflammatory capacity of PDA nanoparticles was further demonstrated in murine models of both acute peritonitis and acute lung injury (ALI), where diminished ROS generation, reduced proinflammatory cytokines, attenuated neutrophil infiltration, and alleviated lung tissue damage were observed in PDA-treated mice after a single dose of PDA treatment. Our work therefore presents the great promise of PDA nanoparticles as a biocompatible nano-drug for anti-inflammation therapy to treat acute inflammation-induced injury

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO