UTILIZING TRANSFERRING LEARNING APPROACH IN SINGLE-CELL RNA SEQUENCING DATA ANALYSIS

The aim of this thesis is to develop theoretical understanding and enhance programming skills in computational biology research. Advanced high-throughput sequencing technologies have rendered data with high dimensionality such as single-cell data. Dimension reduction methods are widely applied to high-dimensional data, with additional analytical approaches we can interpret such data and discover novel biological phenomena during the cell differentiation process. Transferring learning is one of the approaches that can be used to discover associations and heterogeneity between single-cell datasets. This method is used to explore multiple datasets at the same time and facilitates a better understanding of the complicated cell differentiation process

An Image Based Visual Servo Method for Probe-and-Drogue Autonomous Aerial Refueling

With the high focus on autonomous aerial refueling recently, it becomes increasingly urgent to design efficient methods or algorithms to solve AAR problems in complicated aerial environments. Apart from the complex aerodynamic disturbance, another problem is the pose estimation error caused by the camera calibration error, installation error, or 3D object modeling error, which may not satisfy the highly accurate docking. The main objective of the effort described in this paper is the implementation of an image-based visual servo control method, which contains the establishment of an image-based visual servo model involving the receiver's dynamics and the design of the corresponding controller. Simulation results indicate that the proposed method can make the system dock successfully under complicated conditions and improve the robustness against pose estimation error

GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute

In recent years, point clouds have become increasingly popular for representing three-dimensional (3D) visual objects and scenes. To efficiently store and transmit point clouds, compression methods have been developed, but they often result in a degradation of quality. To reduce color distortion in point clouds, we propose a graph-based quality enhancement network (GQE-Net) that uses geometry information as an auxiliary input and graph convolution blocks to extract local features efficiently. Specifically, we use a parallel-serial graph attention module with a multi-head graph attention mechanism to focus on important points or features and help them fuse together. Additionally, we design a feature refinement module that takes into account the normals and geometry distance between points. To work within the limitations of GPU memory capacity, the distorted point cloud is divided into overlap-allowed 3D patches, which are sent to GQE-Net for quality enhancement. To account for differences in data distribution among different color omponents, three models are trained for the three color components. Experimental results show that our method achieves state-of-the-art performance. For example, when implementing GQE-Net on the recent G-PCC coding standard test model, 0.43 dB, 0.25 dB, and 0.36 dB Bjontegaard delta (BD)-peak-signal-to-noise ratio (PSNR), corresponding to 14.0%, 9.3%, and 14.5% BD-rate savings can be achieved on dense point clouds for the Y, Cb, and Cr components, respectively.Comment: 13 pages, 11 figures, submitted to IEEE TI

On Routing Optimization in Networks with Embedded Computational Services

Modern communication networks are increasingly equipped with in-network computational capabilities and services. Routing in such networks is significantly more complicated than the traditional routing. A legitimate route for a flow not only needs to have enough communication and computation resources, but also has to conform to various application-specific routing constraints. This paper presents a comprehensive study on routing optimization problems in networks with embedded computational services. We develop a set of routing optimization models and derive low-complexity heuristic routing algorithms for diverse computation scenarios. For dynamic demands, we also develop an online routing algorithm with performance guarantees. Through evaluations over emerging applications on real topologies, we demonstrate that our models can be flexibly customized to meet the diverse routing requirements of different computation applications. Our proposed heuristic algorithms significantly outperform baseline algorithms and can achieve close-to-optimal performance in various scenarios.Comment: 16 figure

Docking control for probe-drogue refueling: An additive-state-decomposition-based output feedback iterative learning control method

Designing a controller for the docking maneuver in Probe-Drogue Refueling (PDR) is an important but challenging task, due to the complex system model and the high precision requirement. In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control (ILC) is adopted in this paper. First, Additive State Decomposition (ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of NonMinimum Phase (NMP) by separating these features into two subsystems (a primary system and a secondary system). After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant (LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation. The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system. Furthermore, to compensate for the receiver-independent uncertainties, a correction action is proposed by using the terminal docking error, which can lead to a smaller docking error at the docking moment. Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR

A cuproptosis random forest cox score model-based evaluation of prognosis, mutation characterization, immune infiltration, and drug sensitivity in hepatocellular carcinoma

BackgroundHepatocellular carcinoma is the third most deadly malignant tumor in the world with a poor prognosis. Although immunotherapy represents a promising therapeutic approach for HCC, the overall response rate of HCC patients to immunotherapy is less than 30%. Therefore, it is of great significance to explore prognostic factors and investigate the associated tumor immune microenvironment features.MethodsBy analyzing RNA-seq data of the TCGA-LIHC cohort, the set of cuproptosis related genes was extracted via correlation analysis as a generalization feature. Then, a random forest cox prognostic model was constructed and the cuproptosis random forest cox score was built by random forest feature filtering and univariate multivariate cox regression analysis. Subsequently, the prognosis prediction of CRFCS was evaluated via analyzing data of independent cohorts from GEO and ICGC by using KM and ROC methods. Moreover, mutation characterization, immune cell infiltration, immune evasion, and drug sensitivity of CRFCS in HCC were assessed.ResultsA cuproptosis random forest cox score was built based on a generalization feature of four cuproptosis related genes. Patients in the high CRFCS group exhibited a lower overall survival. Univariate multivariate Cox regression analysis validated CRFCS as an independent prognostic indicator. ROC analysis revealed that CRFCS was a good predictor of HCC (AUC =0.82). Mutation analysis manifested that microsatellite instability (MSI) was significantly increased in the high CRFCS group. Meanwhile, tumor microenvironment analysis showed that the high CRFCS group displayed much more immune cell infiltration compared with the low CRFCS group. The immune escape assessment analysis demonstrated that the high CRFCS group displayed a decreased TIDE score indicating a lower immune escape probability in the high CRFCS group compared with the low CRFCS group. Interestingly, immune checkpoints were highly expressed in the high CRFCS group. Drug sensitivity analysis revealed that HCC patients from the high CRFCS group had a lower IC50 of sorafenib than that from the low CRFCS group.ConclusionsIn this study, we constructed a cuproptosis random forest cox score (CRFCS) model. CRFCS was revealed to be a potential independent prognostic indicator of HCC and high CRFCS samples showed a poor prognosis. Interestingly, CRFCS were correlated with TME characteristics as well as clinical treatment efficacy. Importantly, compared with the low CRFCS group, the high CRFCS group may benefit from immunotherapy and sorafenib treatment

Chinese Expert Consensus on Critical Care Ultrasound Applications at COVID-19 Pandemic

The spread of new coronavirus (SARS-Cov-2) follows a different pattern than previous respiratory viruses, posing a serious public health risk worldwide. World Health Organization (WHO) named the disease as COVID-19 and declared it a pandemic. COVID-19 is characterized by highly contagious nature, rapid transmission, swift clinical course, profound worldwide impact, and high mortality among critically ill patients. Chest X-ray, computerized tomography (CT), and ultrasound are commonly used imaging modalities. Among them, ultrasound, due to its portability and non-invasiveness, can be easily moved to the bedside for examination at any time. In addition, with use of 4G or 5G networks, remote ultrasound consultation can also be performed, which allows ultrasound to be used in isolated medial areas. Besides, the contact surface of ultrasound probe with patients is small and easy to be disinfected. Therefore, ultrasound has gotten lots of positive feedbacks from the frontline healthcare workers, and it has played an indispensable role in the course of COVID-19 diagnosis and follow up

Dirac Leptogenesis with a Non-anomalous U(1)′U(1)^{\prime} Family Symmetry

We propose a model for Dirac leptogenesis based on a non-anomalous $U(1)^{\prime}$ gauged family symmetry. The anomaly cancellation conditions are satisfied with no new chiral fermions other than the three right-handed neutrinos, giving rise to stringent constraints among the charges. Realistic masses and mixing angles are obtained for all fermions. The model predicts neutrinos of the Dirac type with naturally suppressed masses. Dirac leptogenesis is achieved through the decay of the flavon fields. The cascade decays of the vector-like heavy fermions in the Froggatt-Nielsen mechanism play a crucial role in the separation of the primodial lepton numbers. We find that a large region of parameter space of the model gives rise to a sufficient cosmological baryon number asymmetry through Dirac leptogenesis.Comment: 8 pages, 8 figures, version to appear in JHE