Deep Learning in Single-Cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

One-Shot Simple Pattern Detection without Pre-Training and Gradient-Based Strategy

One-shot object detection has been a highly demanded yet challenging task since the early age of convolutional neural networks (CNNs). For some newly started projects, a handy network that can learn the target’s pattern using a single picture and automatically decide its architecture is needed. To specifically address a scenario in which a single or multiple targets are standing in relatively stable circumstances with hardly any training data, where the rough location of the target is required, we propose a one-shot simple target detection model that focuses on two main tasks: (1) deciding if the target is in the testing image, and (2) if yes, outputting the target’s location in the image. This model requires no pre-training and decides its architecture automatically; therefore, it could be applied to a newly started target detection project with unconventionally simple targets and few training examples. We also propose an architecture with a non-training parameter-gaining strategy and correlation coefficient-based feedforward and activation functions, as well as easy interpretability, which might provide a perspective on studies in neural networks. We tested this design on the data we collected in our project, the Brown–Yosemite dataset and part of the Mnist dataset. It successfully returned the target area in our project and obtained an IOU of up to 87.04%, reached 80.28% accuracy on the Brown–Yosemite dataset with disposable networks, and obtained an accuracy of up to 89.4% on part of the Mnist dataset in the detection task

Computer-aided Design for the Route of the Test Waveguides

With the rapid development of China's space industry, digitization and intelligent is the tendency of the future. The applications of the waveguide are gradually widespread. During the thermal test phase, the routes of the test waveguides are similar for each spacecraft. Although the waveguides are highly standardized, so far it needs engineers to design the particular route of the test waveguidess, then map the engineering drawing for every test. In order to efficiently design the route of waveguide, it needs to design an application to help the engineers. With the help of the MFC(Microsoft Foundation Classes) and the pro/toolkit, it is easily to do the modeling and simulation. After automatic design the particular the route of the waveguide, the API of AutoCAD type library is used to help to modify the engineer drawing. Engineers can supervise every step of this application, and easily to modify the key parameters

The Impact of Urbanization on Mesoscale Convective Systems in the Yangtze River Delta Region of China: Insights Gained From Observations and Modeling

Urbanization is an important factor that may influence the formation and development of clouds and precipitation. In this study, we focus on studying the influence of urbanization on mesoscale convective systems (MCS) over the Yangtze River Delta region in China under different synoptic conditions using a combination of radiosonde, meteorological station, and satellite observations. It demonstrates that synoptic forcing can be used to distinguish the effect of land cover and land use on MCS. When the synoptic-scale forcing is weak, the urban heat island (UHI) is the main factor affecting the vertical development of clouds. The UHI decreases atmospheric stability and enhances horizontal convergence, invigorating clouds over and downwind of cities. On the other hand, when strong synoptic-scale forcing is present, buildings in cities cause clouds to bifurcate upwind of cities, moving around them, primarily through their dynamic effects. The heights of cloud tops in central and downwind parts of cities thus drop. Using the Weather Research and Forecasting model simulations of different atmospheric forcings also demonstrate similar patterns around major urban areas. The joint analyses of observations and model simulations provide new insights into the net effects of urbanization on cloud systems.https://doi.org/10.1029/2022JD03770

Fine-Grained Recognition of Surface Targets with Limited Data

Recognition of surface targets has a vital influence on the development of military and civilian applications such as maritime rescue patrols, illegal-vessel screening, and maritime operation monitoring. However, owing to the interference of visual similarity and environmental variations and the lack of high-quality datasets, accurate recognition of surface targets has always been a challenging task. In this paper, we introduce a multi-attention residual model based on deep learning methods, in which channel and spatial attention modules are applied for feature fusion. In addition, we use transfer learning to improve the feature expression capabilities of the model under conditions of limited data. A function based on metric learning is adopted to increase the distance between different classes. Finally, a dataset with eight types of surface targets is established. Comparative experiments on our self-built dataset show that the proposed method focuses more on discriminative regions, avoiding problems like gradient disappearance, and achieves better classification results than B-CNN, RA-CNN, MAMC, and MA-CNN, DFL-CNN

AFFPN: Attention Fusion Feature Pyramid Network for Small Infrared Target Detection

The detection of small infrared targets lacking texture and shape information in the presence of complex background clutter is a challenge that has attracted considerable research attention in recent years. Typical deep learning-based target detection methods are designed with deeper network structures, which may lose targets in the deeper layers and cannot directly be used for small infrared target detection. Therefore, we designed the attention fusion feature pyramid network (AFFPN) specifically for small infrared target detection. Specifically, it consists of feature extraction and feature fusion modules. In the feature extraction stage, the global contextual prior information of small targets is first considered in the deep layer of the network using the atrous spatial pyramid pooling module. Subsequently, the spatial location and semantic information features of small infrared targets in the shallow and deep layers are adaptively enhanced by the designed attention fusion module to improve the feature representation capability of the network for targets. Finally, high-performance detection is achieved through the multilayer feature fusion mechanism. Moreover, we performed a comprehensive ablation study to evaluate the effectiveness of each component. The results demonstrate that the proposed method performs better than state-of-the-art methods on a publicly available dataset. Furthermore, AFFPN was deployed on an NVIDIA Jetson AGX Xavier development board and achieved real-time target detection, further advancing practical research and applications in the field of unmanned aerial vehicle infrared search and tracking

Barriers to the Prevention and Control of Hepatitis B and Hepatitis C in the Community of Southwestern China: A Qualitative Research

Objective viral hepatitis is a big challenge in China. However, few studies have focused on mapping the difficulties from a broader view. This study aimed to identify the barriers to the prevention and control of hepatitis B and hepatitis C in communities from the perspectives of hepatitis patients, residents, and healthcare providers. A total of 26 participants were recruited through purposive sampling. Data were collected by in-depth face-to-face interviews from September 2015 to May 2016 in two communities from Chongqing and Chengdu, China. A thematic framework was applied to analyze the qualitative data from the interviews. The critical factors of barriers to hepatitis prevention and control in the districts included poor cognition of residents regarding hepatitis B and hepatitis C, severe stigma in society, inadequate health education, and the provision of unsatisfactory medical services. Strengthening health education and improving services for treating patients with hepatitis are suggested to make further progress. A substantial gap remains between the need and currently available services for hepatitis patients and residents. Delivering quality prevention and control health services, improving health education, and reducing stigma in society are recommended to improve the prevention and control program for hepatitis B and C in communities

Barriers to the Prevention and Control of Hepatitis B and Hepatitis C in the Community of Southwestern China: A Qualitative Research

Objective viral hepatitis is a big challenge in China. However, few studies have focused on mapping the difficulties from a broader view. This study aimed to identify the barriers to the prevention and control of hepatitis B and hepatitis C in communities from the perspectives of hepatitis patients, residents, and healthcare providers. A total of 26 participants were recruited through purposive sampling. Data were collected by in-depth face-to-face interviews from September 2015 to May 2016 in two communities from Chongqing and Chengdu, China. A thematic framework was applied to analyze the qualitative data from the interviews. The critical factors of barriers to hepatitis prevention and control in the districts included poor cognition of residents regarding hepatitis B and hepatitis C, severe stigma in society, inadequate health education, and the provision of unsatisfactory medical services. Strengthening health education and improving services for treating patients with hepatitis are suggested to make further progress. A substantial gap remains between the need and currently available services for hepatitis patients and residents. Delivering quality prevention and control health services, improving health education, and reducing stigma in society are recommended to improve the prevention and control program for hepatitis B and C in communities