Peptoid morphology studies and its performance in inhibiting islet amyloid polypeptide

Abstract 1 Proteins have various arrays of functions in human bodies. The bio-functions of proteins are depend on their amino acid sequences and folding processes that affect protein structure. While most of proteins fold in their own pathways, some of them misfold and aggregate due to mutations caused by various reasons. These aggregations are believed to be main causes of protein conformation disorders like Alzheimer\u27s disease, Type II diabetes, Huntington\u27s disease, and prion disease. An aggregated form of amyloid beta is considered as a hallmark of Alzheimer\u27s disease. Amyloid beta develops from oligomer into a fibril as the aggregation process proceeds. The fibrils interrupt communications between brain cells. In a previous research (Dr. Servoss, et al.) it was proven that the aggregation of amyloid beta can be inhibited by a peptoid that mimics a sequence of amyloid binding sight (KLVFF). The peptoid (JPT1) successfully inhibited both beta sheet and fibril formation of amyloid beta in vitro. In this experiment, two variants of JPT1 were synthesized to observe how the structure of JPT1 affect the aggregation of amyloid beta: side chain rearrangement (JPT1s), achiral form (JPT1a). Amyloid beta samples were treated with the variants of JPT1 and compared with one treated with JPT1. Both beta sheet and fibril structures of amyloid beta were detected through ThT assay and dot blots respectively. In a result, JPT1a was characterized with higher inhibition rates against both beta sheet structures and fibrils than those of JPT1. While JPT1a showed a significant improve in inhibition rate, JPT1s did not show a noticeable difference with JPT1 in inhibiting amyloid beta aggregation. Later, it was observed that the amyloid beta samples, which are treated with JPT1a, took a different pathway in aggregation process. They were developed into high ordered oligomers instead of fibrils. This result implies that the the chiral structure of JPT1 plays a critical role in inhibiting amyloid beta aggregations. Abstract 2 Type 2 diabetes (T2D) is a disease that is characterized with a protein misfold. T2D brings a disorder in absorbing IAPP in a pancreatic beta cell. A protein IAPP (Islet Amyloid Polypeptide), also known as amyline, is related with suppression of food intake and gastric emptying. It is believed that the fibril forms of amylin contributes to the aggravation of pancreatic disorder as they aggregate. Although amylin has different amino acid sequences with amyloid beta, its folding mechanism is similar with that of amyloid beta protein. Amylin forms into a fibril form via beta sheet structure due to misfold. This research was conducted to observe whether JPT1 can inhibit the aggregation of amyloid beta by interrupting pi-pi stacking of beta structures. Different concentrations of JPT1 (0, 40, 100, 200 μM) were added to amylin samples and incubated on an orbit shaker. Both aminated and free acid versions of amylin were tested since the ratio between two amylin is not well understood yet. Before ThT assay, competition assay was proceeded to ensure that the JPT1 does not affect the binding sight of ThT and amylin. Through ThT data and TEM images, clear decreases of amylin beta sheet structure and fibril were observed. However, an increase in beta sheet formation was detected as a high concentration (200 μM ) of amylin was added to free acid amylin. The TEM image of free acid amylin treated with 200 μM of JPT1 suggested a new morphology of amylin aggregation. The experimental data concludes that JPT1 can inhibit amylin aggregation It is also observed that the high concentration of inhibitor can lead to different morphology of aggregates. This results correlates to the previous research where JPT1a inhibited beta sheet structures and fibrils form of amyloid beta effectively, but changed the protein into a high ordered oligomer form

Deep Learning approaches for Robotic Grasp Detection and Image Super-Resolution

Department of Electrical EngineeringIn recent years, many papers mentioned that use Deep learning to objects detection and robot grasping detection have improved accuracy with higher image resolutions. We use the Deep learning to describe robot grasp detection and image supre-resolution related two papers. 0.0.1 Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images Robotic grasp detection for novel objects is a challenging task, but for the last few years, deep learning based approaches have achieved remarkable performance improvements, up to 96.1% accuracy, with RGB-D data. In this paper, we propose fully convolutional neural network (FCNN) based methods for robotic grasp detection. Our methods also achieved state-of-the-art detection accuracy (up to 96.6%) with state-of-the-art real-time computation time for high-resolution images (6-20ms per 360 360 image) on Cornell dataset. Due to FCNN, our proposed method can be applied to images with any size for detecting multigrasps on multiobjects. Proposed methods were evaluated using 4-axis robot arm with small parallel gripper and RGB-D camera for grasping challenging small, novel objects. With accurate visionrobot coordinate calibration through our proposed learning-based, fully automatic approach, our proposed method yielded 90% success rate. 0.0.2 Efficient Module Based Single Image Super Resolution for Multiple Problems Example based single image super resolution (SR) is a fundamental task in computer vision. It is challenging, but recently, there have been significant performance improvements using deep learning approaches. In this article, we propose efficient module based single image SR networks (EMBSR) and tackle multiple SR problems in NTIRE 2018 challenge by recycling trained networks. Our proposed EMBSR allowed us to reduce training time with effectively deeper networks, to use modular ensemble for improved performance, and to separate subproblems for better erformance. We also proposed EDSR-PP, an improved version of previous ESDR by incorporating pyramid pooling so that global as well as local context information can be utilized. Lastly, we proposed a novel denoising / deblurring residual convolutional network (DnResNet) using residual block and batch normalization. Our proposed EMBSR with DnResNet demonstrated that multiple SR problems can be tackled efficiently and e ectively by winning the 2nd place for Track 2 and the 3rd place for Track 3. Our proposed method with EDSR-PP also achieved the ninth place for Track 1 with the fastest run time among top nine teams.clos

On the Explanation of Factors Affecting E-Commerce Adoption

The Internet has grown at a remarkable pace since the emergence of the World Wide Web in the early 1990s. While electronic commerce (e-Commerce) has become an important issue with the growth of the Internet, there has been insufficient empirical research concerning its adoption by Internet users. In this paper, we propose the e-Commerce Adoption Model (e-CAM), which attempts to examine important factors that predict a consumerís online purchasing behavior. e-CAM integrates the technology acceptance model with the theories of perceived risk to explain the adoption of e-Commerce. Specifically, we examine the impact of the following factors on the consumerís purchasing behavior: perceived ease of use, perceived usefulness, perceived risk with products/services, and perceived risk in the context of online transaction. We test the e-CAM model using the structural equation modeling technique. Most of the causal relationships between the constructs postulated by our model are well supported, accounting for 33.4% of the total variance in e-Commerce adoption. In sum, our study finds that all of the antecedent constructs directly and/or indirectly affect the consumerís adoption of e-Commerce. Therefore, the findings suggest that firms providing products/services through e- Commerce should consider these contextual factors in order to facilitate consumersí adoption behavior