Semiconducting transition metal dichalcogenides (TMDs) are promising materials for photodetection over a wide range of visible wavelengths. Photodetection is generally realized via a phototransistor, photoconductor, p-n junction photovoltaic device, and thermoelectric device. The photodetectivity, which is a primary parameter in photodetector design, is often limited by either low photoresponsivity or a high dark current in TMDs materials. Here, we demonstrated a highly sensitive photodetector with a MoS/h-BN/graphene heterostructure, by inserting a h-BN insulating layer between graphene electrode and MoS photoabsorber, the dark-carriers were highly suppressed by the large electron barrier (2.7 eV) at the graphene/h-BN junction while the photocarriers were effectively tunneled through small hole barrier (1.2 eV) at the MoS/h-BN junction. With both high photocurrent/dark current ratio (>10) and high photoresponsivity (180 AW), ultrahigh photodetectivity of 2.6 × 10 Jones was obtained at 7 nm thick h-BN, about 100-1000 times higher than that of previously reported MoS-based devices.

Concepts of non-volatile memory to replace conventional flash memory have suffered from low material reliability and high off-state current, and the use of a thick, rigid blocking oxide layer in flash memory further restricts vertical scale-up. Here, we report a two-terminal floating gate memory, tunnelling random access memory fabricated by a monolayer MoS2/h-BN/monolayer graphene vertical stack. Our device uses a two-terminal electrode for current flow in the MoS2 channel and simultaneously for charging and discharging the graphene floating gate through the h-BN tunnelling barrier. By effective charge tunnelling through crystalline h-BN layer and storing charges in graphene layer, our memory device demonstrates an ultimately low off-state current of 10−14 A, leading to ultrahigh on/off ratio over 109, about ∼103 times higher than other two-terminal memories. Furthermore, the absence of thick, rigid blocking oxides enables high stretchability (>19%) which is useful for soft electronics.

Objective. Hypoxia-inducible factor 2␣ (HIF-2␣) (encoded by Epas1) causes osteoarthritic (OA) cartilage destruction by regulating the expression of catabolic factor genes. We undertook this study to explore the role of interleukin-6 (IL-6) in HIF-2␣-mediated OA cartilage destruction in mice.Methods. The expression of HIF-2␣, IL-6, and catabolic factors was determined at the messenger RNA and protein levels in primary culture mouse chondrocytes, human OA cartilage, and mouse experimental OA cartilage. Experimental OA in wild-type, HIF-2␣-knockdown (Epas1 ؉/؊ ), and Il6 -/-mice was caused by intraarticular injection of Epas1 adenovirus or destabilization of the medial meniscus. The role of IL-6 was determined by treating with recombinant IL-6 protein or by injecting HIF-2␣ adenovirus (AdEpas1) intraarticularly in mice with or without IL-6-neutralizing antibody. Osteoarthritis (OA) is a degenerative joint disorder that is primarily characterized by articular cartilage destruction. However, no effective medical therapy to prevent OA cartilage destruction is currently available, owing to the limited understanding of the underlying molecular pathogenic mechanisms. A variety of potential OA-causing mechanisms have been proposed (1). Biophysical and biochemical factors, such as mechanical stress and proinflammatory cytokines, respectively, are responsible for disruption of cartilage homeostasis and initiation of the catabolic pathway. This in turn leads to activation of biochemical pathways in chondrocytes, a unique resident cell type that synthesizes cartilagespecific extracellular matrix (ECM) components as well as various catabolic and anabolic factors. Activation of biochemical pathways involves the production of proinflammatory cytokines, inflammation, degradation of the ECM by matrix metalloproteinases (MMPs) and ADAMTS, and cessation of ECM synthesis via dedifferentiation and apoptosis of chondrocytes (1,2). Results. We found thatRecently, we (3) and Saito et al (4) demonstrated

Automatic detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size and color, and the existence of various polyp-like mimics during colonoscopy. In this study, we apply a recent region based convolutional neural network (CNN) approach for the automatic detection of polyps in images and videos obtained from colonoscopy examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system. To overcome the polyp detection obstacles and the small number of polyp images, we examine image augmentation strategies for training deep networks. We further propose two efficient post-learning methods such as, automatic false positive learning and off-line learning, both of which can be incorporated with the region based detection system for reliable polyp detection. Using the large size of colonoscopy databases, experimental results demonstrate that the suggested detection systems show better performance compared to other systems in the literature. Furthermore, we show improved detection performance using the proposed post-learning schemes for colonoscopy videos.

Hypoxia-inducible factor-2α (HIF-2α) is sufficient to cause experimental rheumatoid arthritis and acts to regulate the functions of fibroblast-like cells from tissue surrounding joints, independent of HIF-1α.