202 research outputs found
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 Family Symmetry
We propose a model for Dirac leptogenesis based on a non-anomalous
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
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