Neuromorphic Systems for Pattern Recognition and Uav Trajectory Planning

Detection and control are two essential components in an intelligent system. This thesis investigates novel techniques in both areas with a focus on the applications of handwritten text recognition and UAV flight control. Recognizing handwritten texts is a challenging task due to many different writing styles and lack of clear boundary between adjacent characters. The difficulty is greatly increased if the detection algorithms is solely based on pattern matching without information of dynamics of handwriting trajectories. Motivated by the aforementioned challenges, this thesis first investigates the pattern recognition problem. We use offline handwritten texts recognition as a case study to explore the performance of a recurrent belief propagation model. We first develop a probabilistic inference network to post process the recognition results of deep Convolutional Neural Network (CNN) (e.g. LeNet) and collect individual characters to form words. The output of the inference network is a set of words and their probability. A series of post processing and improvement techniques are then introduced to further increase the recognition accuracy. We study the performance of proposed model through various comparisons. The results show that it significantly improves the accuracy by correcting deletion, insertion and replacement errors, which are the main sources of invalid candidate words. Deep Reinforcement Learning (DRL) has widely been applied to control the autonomous systems because it provides solutions for various complex decision-making tasks that previously could not be solved solely with deep learning. To enable autonomous Unmanned Aerial Vehicles (UAV), this thesis presents a two-level trajectory planning framework for UAVs in an indoor environment. A sequence of waypoints is selected at the higher-level, which leads the UAV from its current position to the destination. At the lower-level, an optimal trajectory is generated analytically between each pair of adjacent waypoints. The goal of trajectory generation is to maintain the stability of the UAV, and the goal of the waypoints planning is to select waypoints with the lowest control thrust throughout the entire trip while avoiding collisions with obstacles. The entire framework is implemented using DRL, which learns the highly complicated and nonlinear interaction between those two levels, and the impact from the environment. Given the pre-planned trajectory, this thesis further presents an actor-critic reinforcement learning framework that realizes continuous trajectory control of the UAV through a set of desired waypoints. We construct a deep neural network and develop reinforcement learning for better trajectory tracking. In addition, Field Programmable Gate Arrays (FPGA) based hardware acceleration is designed for energy efficient real-time control. If we are to integrate the trajectory planning model onto a UAV system for real-time on-board planning, a key challenge is how to deliver required performance under strict memory and computational constraints. Techniques that compress Deep Neural Network (DNN) models attract our attention because they allow optimized neural network models to be efficiently deployed on platforms with limited energy and storage capacity. However, conventional model compression techniques prune the DNN after it is fully trained, which is very time-consuming especially when the model is trained using DRL. To overcome the limitation, we present an early phase integrated neural network weight compression system for DRL based waypoints planning. By applying pruning at an early phase, the compression of the DRL model can be realized without significant overhead in training. By tightly integrating pruning and retraining at the early phase, we achieve a higher model compression rate, reduce more memory and computing complexity, and improve the success rate compared to the original work

Effects of High Intensity Interval Training on Children’s Health and Physical Fitness

The effect of high intensity interval training on children’s health is the focus of experts and scholars worldwide. After the outline of “Building a Strong Country in Sports” was issued in 2019, China’s attention to sports has reached a peak. However, the proportion of obese or myopic children in schools at all levels is still high, and the problem of low physical fitness of Chinese students is still serious. The purpose of this study was to review previous studies to explore the effects of high intensity interval training on children’s health and physical fitness. We reviewed relevant literature both in Chinese and English, sorted out and summarized the research results. We found that high intensity interval training has a positive impact on children’s health and physical fitness, can significantly improve children’s muscle strength and cardiopulmonary endurance, and can effectively reduce fat mass in overweight and obese children. Meanwhile, the high intensity interval training has higher training pleasure. Therefore, high intensity interval training can be used as an effective exercise method for children. It is suggested to handle the relationship scientifically and reasonably among individual differences, training time, and training intensity. We should pay attention to the group training workload for overweight and obese children, adopt the principle of gradual improvement, and gradually improve the ability and health level of overweight and obese children

CS-SELEX Generates High-Affinity ssDNA Aptamers as Molecular Probes for Hepatitis C Virus Envelope Glycoprotein E2

Currently, the development of effective diagnostic reagents as well as treatments against Hepatitis C virus (HCV) remains a high priority. In this study, we have described the development of an alive cell surface -Systematic Evolution of Ligands by Exponential Enrichment (CS-SELEX) technique and screened the functional ssDNA aptamers that specifically bound to HCV envelope surface glycoprotein E2. Through 13 rounds of selection, the CS-SELEX generated high-affinity ssDNA aptamers, and the selected ssDNA aptamer ZE2 demonstrated the highest specificity and affinity to E2-positive cells. HCV particles could be specifically captured and diagnosed using the aptamer ZE2. A good correlation was observed in HCV patients between HCV E2 antigen-aptamer assay and assays for HCV RNA quantities or HCV antibody detection. Moreover, the selected aptamers, especially ZE2, could competitively inhibit E2 protein binding to CD81, an important HCV receptor, and significantly block HCV cell culture (HCVcc) infection of human hepatocytes (Huh7.5.1) in vitro. Our data demonstrate that the newly selected ssDNA aptamers, especially aptamer ZE2, hold great promise for developing new molecular probes, as an early diagnostic reagent for HCV surface antigen, or a therapeutic drug specifically for HCV

Cationic Polystyrene Resolves Nonalcoholic Steatohepatitis, Obesity, and Metabolic Disorders by Promoting Eubiosis of Gut Microbiota and Decreasing Endotoxemia.

A pandemic of metabolic diseases, consisting of type 2 diabetes, nonalcoholic fatty liver disease, and obesity, has imposed critical challenges for societies worldwide, prompting investigation of underlying mechanisms and exploration of low-cost and effective treatment. In this report, we demonstrate that metabolic disorders in mice generated by feeding with a high-fat diet without dietary vitamin D can be prevented by oral administration of polycationic amine resin. Oral administration of cholestyramine, but not the control uncharged polystyrene, was able to sequester negatively charged bacterial endotoxin in the gut, leading to 1) reduced plasma endotoxin levels, 2) resolved systemic inflammation and hepatic steatohepatitis, and 3) improved insulin sensitivity. Gut dysbiosis, characterized as an increase of the phylum Firmicutes and a decrease of Bacteroidetes and Akkermansia muciniphila, was fully corrected by cholestyramine, indicating that the negatively charged components in the gut are critical for the dysbiosis. Furthermore, fecal bacteria transplant, derived from cholestyramine-treated animals, was sufficient to antagonize the metabolic disorders of the recipient mice. These results indicate that the negatively charged components produced by dysbiosis are critical for biogenesis of metabolic disorders and also show a potential application of cationic polystyrene to treat metabolic disorders through promoting gut eubiosis

Genomic analysis quantifies pyroptosis in the immune microenvironment of HBV-related hepatocellular carcinoma

Pyroptosis, a way of pro-inflammatory death, plays a significant part in the tumor microenvironment (TME). A recent study has shown that the hepatitis C virus changes the TME by inducing pyroptosis against hepatocellular carcinoma (HCC). However, compared to TME in hepatitis C virus-infected HCC, the exploration of immune characteristics and response to immunotherapy associated with the pyroptosis phenotype is still insufficient in hepatitis B virus-related HCC (HBV-HCC). Our study constructed pyroptosis-score (PYS) via principal-component analysis (PCA) to unveil the link between pyroptosis and tumor immunity in 369 HBV-HCC patients. Compared with the low-PYS group, subjects with higher PYS were associated with poor prognosis but were more susceptible to anti-PD-L1 treatment. In addition, we found that PYS can serve independently as a prognostic factor for HBV-HCC, making it possible for us to identify specific small molecule drugs with a potential value in inhibiting tumors via targeting pyroptosis. Also, the target genes predicted by the Weighted gene co-expression network analysis (WGCNA) and pharmacophore model were enriched in the HIF-1 signaling pathway and NF-kB transcription factor activity, which were related to the mechanism of inflammation-driven cancer. The PYS is extremely important in predicting prognosis and responses to immunotherapy. New treatment strategies for inflammation-driven cancers may be found by targeting pyroptosis

Bio-oil based biorefinery strategy for the production of succinic acid

Background: Succinic acid is one of the key platform chemicals which can be produced via biotechnology process instead of petrochemical process. Biomass derived bio-oil have been investigated intensively as an alternative of diesel and gasoline fuels. Bio-oil could be fractionized into organic phase and aqueous phase parts. The organic phase bio-oil can be easily upgraded to transport fuel. The aqueous phase bio-oil (AP-bio-oil) is of low value. There is no report for its usage or upgrading via biological methods. In this paper, the use of AP-bio-oil for the production of succinic acid was investigated