[Exosome and prevention of Alzheimer's disease: based on theory of kidney storing essence].

The theory of kidney storing essence storage, an important part of the basic theory of traditional Chinese medicine(TCM), comes from the Chapter 9 Discussion on Six-Plus-Six System and the Manifestations of the Viscera in the Plain Questions, which says that "the kidney manages closure and is the root of storage and the house of Jing(Essence)". According to this theory, essence is the fundamental substance of human life activities and it is closely related to the growth and development of the human body. Alzheimer's disease(AD) is one of the common neurodegenerative diseases, with the main pathological features of Aß deposition and Tau phosphorylation, which activate neurotoxic reactions and eventually lead to neuronal dysfunction and cell death, severely impairing the patient's cognitive and memory functions. Although research results have been achieved in the TCM treatment of AD, the complex pathogenesis of AD makes it difficult to develop the drugs capable of curing AD. The stem cell therapy is an important method to promote self-repair and regeneration, and bone marrow mesenchymal stem cells(BMSCs) as adult stem cells have the ability of multi-directional differentiation. By reviewing the relevant literature, this paper discusses the association between BMSCs and the TCM theory of kidney storing essence, and expounds the material basis of this theory from the perspective of molecular biology. Studies have shown that TCM with the effect of tonifying the kidney in the treatment of AD are associated with BMSCs. Exosomes produced by such cells are one of the main substances affecting AD. Exosomes containing nucleic acids, proteins, and lipids can participate in intercellular communication, regulate cell function, and affect AD by reducing Aß deposition, inhibiting Tau protein phosphorylation and neuroinflammation, and promoting neuronal regeneration. Therefore, discussing the prevention and treatment of exosomes and AD based on the theory of kidney storing essence will provide a new research idea for the TCM treatment of AD.

Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers-A Systematic Literature Review.

Background: The planetary rover is an essential platform for planetary exploration. Visual semantic segmentation is significant in the localization, perception, and path planning of the rover autonomy. Recent advances in computer vision and artificial intelligence brought about new opportunities. A systematic literature review (SLR) can help analyze existing solutions, discover available data, and identify potential gaps. Methods: A rigorous SLR has been conducted, and papers are selected from three databases (IEEE Xplore, Web of Science, and Scopus) from the start of records to May 2022. The 320 candidate studies were found by searching with keywords and bool operators, and they address the semantic terrain segmentation in the navigation vision of planetary rovers. Finally, after four rounds of screening, 30 papers were included with robust inclusion and exclusion criteria as well as quality assessment. Results: 30 studies were included for the review, and sub-research areas include navigation (16 studies), geological analysis (7 studies), exploration efficiency (10 studies), and others (3 studies) (overlaps exist). Five distributions are extendedly depicted (time, study type, geographical location, publisher, and experimental setting), which analyzes the included study from the view of community interests, development status, and reimplementation ability. One key research question and six sub-research questions are discussed to evaluate the current achievements and future gaps. Conclusions: Many promising achievements in accuracy, available data, and real-time performance have been promoted by computer vision and artificial intelligence. However, a solution that satisfies pixel-level segmentation, real-time inference time, and onboard hardware does not exist, and an open, pixel-level annotated, and the real-world data-based dataset is not found. As planetary exploration projects progress worldwide, more promising studies will be proposed, and deep learning will bring more opportunities and contributions to future studies. Contributions: This SLR identifies future gaps and challenges by proposing a methodical, replicable, and transparent survey, which is the first review (also the first SLR) for semantic terrain segmentation in the navigation vision of planetary rovers.

Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers.

Sky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision.

Development of Gas-Liquid Flow Regimes Identification Using a Noninvasive Ultrasonic Sensor, Belt-Shape Features, and Convolutional Neural Network in an S-Shaped Riser.

The problem of classifying gas-liquid two-phase flow regimes from ultrasonic signals is considered. A new method, belt-shaped features (BSFs), is proposed for performing feature extraction on the preprocessed data. A convolutional neural network (CNN/ConvNet)-based classifier is then applied to categorize into one of the four flow regimes: 1) annular; 2) churn; 3) slug; or 4) bubbly. The proposed ConvNet classifier includes multiple stages of convolution and pooling layers, which both decrease the dimension and learn the classification features. Using experimental data collected from an industrial-scale multiphase flow facility, the proposed ConvNet classifier achieved 97.40%, 94.57%, and 94.94% accuracy, respectively, for the training set, testing set, and validation set. These results demonstrate the applicability of the BSF features and the ConvNet classifier for flow regime classification in industrial applications.