Fabrication of the Kinect Remote-controlled Cars and Planning of the Motion Interaction Courses

AbstractThis paper describes the fabrication of Kinect remote-controlled cars, using PC, Kinect sensor, interface control circuit, embedded controller, and brake device, as well as the planning of motion interaction courses. The Kinect sensor first detects the body movement of the user, and converts it into control commands. Then, the PC sends the commands to Arduino control panel via XBee wireless communication modules. The interface circuit is used to control movement and direction of motors, including forward and backward, left and right. In order to develop the content of Kinect motion interaction courses, this study conducted literature review to understand the curriculum contents, and invited experts for interviews to collect data on learning background, teaching contents and unit contents. Based on the data, the teaching units and outlines are developed for reference of curriculums

Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise

Recently, quantum classifiers have been known to be vulnerable to adversarial attacks, where quantum classifiers are fooled by imperceptible noises to have misclassification. In this paper, we propose one first theoretical study that utilizing the added quantum random rotation noise can improve the robustness of quantum classifiers against adversarial attacks. We connect the definition of differential privacy and demonstrate the quantum classifier trained with the natural presence of additive noise is differentially private. Lastly, we derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples supported by experimental results.Comment: Submitted to IEEE ICASSP 202

ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios

2019 Kidney Tumor Segmentation Challenge: Medical Image Segmentation with Two-Stage Process

Since we are trying to deal with the medical images of real patients, the dataset are usually predominantly composed of ”normal” samples. The target classes only appear in a very small portion of the entire dataset, which leads to the so-called class imbalance problem. Besides, there is only a small percentage of foreground inside the ”abnormal” images. The great majority of background leads the significant detrimental effect on training. In such cases, model tends to focus on learning the dominant classes, leading to the poor prediction of minority class. However, the incorrect classification of pathological images can cause serious consequence in clinical practice

Dual Targeting of 3-Hydroxy-3-methylglutaryl Coenzyme A Reductase and Histone Deacetylase as a Therapy for Colorectal Cancer

AbstractStatins are 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase (HMGR) inhibitors decreasing serum cholesterol and have shown promise in cancer prevention. In this study, we demonstrated the oncogenic role of HMGR in colorectal cancer (CRC) by disclosing increased HMGR activity in CRC patients and its enhancement of anti-apoptosis and stemness. Our previous studies showed that statins containing carboxylic acid chains possessed activity against histone deacetylases (HDACs), and strengthened their anti-HDAC activity through designing HMGR-HDAC dual inhibitors, JMF compounds. These compounds exerted anti-cancer effect in CRC cells as well as in AOM-DSS and ApcMin/+ CRC mouse models. JMF mostly regulated the genes related to apoptosis and inflammation through genome-wide ChIP-on-chip analysis, and Ingenuity Pathways Analysis (IPA) predicted their respective regulation by NR3C1 and NF-κB. Furthermore, JMF inhibited metastasis, angiogenesis and cancer stemness, and potentiated the effect of oxaliplatin in CRC mouse models. Dual HMGR-HDAC inhibitor could be a potential treatment for CRC

Biomechanical Considerations in the Design of High-Flexion Total Knee Replacements

Typically, joint arthroplasty is performed to relieve pain and improve functionality in a diseased or damaged joint. Total knee arthroplasty (TKA) involves replacing the entire knee joint, both femoral and tibial surfaces, with anatomically shaped artificial components in the hope of regaining normal joint function and permitting a full range of knee flexion. In spite of the design of the prosthesis itself, the degree of flexion attainable following TKA depends on a variety of factors, such as the joint’s preoperative condition/flexion, muscle strength, and surgical technique. High-flexion knee prostheses have been developed to accommodate movements that require greater flexion than typically achievable with conventional TKA; such high flexion is especially prevalent in Asian cultures. Recently, computational techniques have been widely used for evaluating the functionality of knee prostheses and for improving biomechanical performance. To offer a better understanding of the development and evaluation techniques currently available, this paper aims to review some of the latest trends in the simulation of high-flexion knee prostheses

Large-scale patterned quantum dots as color conversion layer for organic light emitting diode by inkjet printing

In this work, we fabricated 6.6-inch QD display panel by inkjet printing technology, being cooperated with active matrix organic light emitting diodes (AMOLEDs). Here 3-stack blue OLEDs (BOLEDs) with top-emission structure acted as backlight and red QD layer acted as converted materials, which exhibited high quantum efficiency, high luminance, high color purity and improved wide viewing angle of output emission. We believe that inkjet-printed QD display with AMOLEDs would be promising candidate for the next generation display and lighting in the near future

Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes

Amyloid-Beta (Aβ) D7H Mutation Increases Oligomeric Aβ42 and Alters Properties of Aβ-Zinc/Copper Assemblies

Amyloid precursor protein (APP) mutations associated with familial Alzheimer's disease (AD) usually lead to increases in amyloid β-protein (Aβ) levels or aggregation. Here, we identified a novel APP mutation, located within the Aβ sequence (AβD7H), in a Taiwanese family with early onset AD and explored the pathogenicity of this mutation. Cellular and biochemical analysis reveal that this mutation increased Aβ production, Aβ42/40 ratio and prolonged Aβ42 oligomer state with higher neurotoxicity. Because the D7H mutant Aβ has an additional metal ion-coordinating residue, histidine, we speculate that this mutation may promote susceptibility of Aβ to ion. When co-incubated with Zn2+ or Cu2+, AβD7H aggregated into low molecular weight oligomers. Together, the D7H mutation could contribute to AD pathology through a “double punch” effect on elevating both Aβ production and oligomerization. Although the pathogenic nature of this mutation needs further confirmation, our findings suggest that the Aβ N-terminal region potentially modulates APP processing and Aβ aggregation, and further provides a genetic indication of the importance of Zn2+ and Cu2+ in the etiology of AD