Lipid Metabolism in Liver Cancer

Hepatocellular carcinoma (HCC) represents 90% cases of liver cancer that is the second leading cause of cancer death in the world. With the pandemic of obesity and other metabolic syndromes in both adults and children, the incidences of fatty liver diseases and the derived HCC are on their upward track. Emerging metabolomic studies have revealed the perturbation of lipid profiles and other metabolites in fatty liver diseases and HCC. Two common metabolic features including enforced fatty acid oxidation and glycolysis could distinguish HCC from healthy liver and chronic non-tumor liver diseases. The potential translational impacts of fatty acid oxidation are gaining great interests, because many recent investigations have demonstrated that tumor cells were dependent on fatty acid oxidation for cell survival and tumor growth. Blockage of fatty acid oxidation could sensitize to metabolic stress-induced cell death and tumor growth inhibition. Thus, lipid catabolism, in terms of fatty oxidation, is tuned for tumor maintenance but vulnerable to pharmacological disruption. The therapeutic potentials of blocking fatty acid oxidation are yet to be further carefully examined

GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning

This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein, the model is trained to recognize multiple unseen classes within a sample (e.g., an image) based on seen classes and auxiliary knowledge, e.g., semantic information. Existing methods usually resort to analyzing the relationship of various seen classes residing in a sample from the dimension of spatial or semantic characteristics, and transfer the learned model to unseen ones. But they ignore the effective integration of local and global features. That is, in the process of inferring unseen classes, global features represent the principal direction of the image in the feature space, while local features should maintain uniqueness within a certain range. This integrated neglect will make the model lose its grasp of the main components of the image. Relying only on the local existence of seen classes during the inference stage introduces unavoidable bias. In this paper, we propose a novel and effective group bi-enhancement framework for MLZSL, dubbed GBE-MLZSL, to fully make use of such properties and enable a more accurate and robust visual-semantic projection. Specifically, we split the feature maps into several feature groups, of which each feature group can be trained independently with the Local Information Distinguishing Module (LID) to ensure uniqueness. Meanwhile, a Global Enhancement Module (GEM) is designed to preserve the principal direction. Besides, a static graph structure is designed to construct the correlation of local features. Experiments on large-scale MLZSL benchmark datasets NUS-WIDE and Open-Images-v4 demonstrate that the proposed GBE-MLZSL outperforms other state-of-the-art methods with large margins.Comment: 11 pages, 8 figure

Nonlinear sub-cyclotron resonance as a formation mechanism for gaps in banded chorus

An interesting characteristic of magnetospheric chorus is the presence of a frequency gap at $\omega \simeq 0.5\Omega_e$, where $\Omega_e$ is the electron cyclotron angular frequency. Recent chorus observations sometimes show additional gaps near $0.3\Omega_e$ and $0.6\Omega_e$. Here we present a novel nonlinear mechanism for the formation of these gaps using Hamiltonian theory and test-particle simulations in a homogeneous, magnetized, collisionless plasma. We find that an oblique whistler wave with frequency at a fraction of the electron cyclotron frequency can resonate with electrons, leading to effective energy exchange between the wave and particles

The Design and Construction of K11: A Novel α-Helical Antimicrobial Peptide

Amphipathic α-helical antimicrobial peptides comprise a class of broad-spectrum agents that are used against pathogens. We designed a series of antimicrobial peptides, CP-P (KWKSFIKKLTSKFLHLAKKF) and its derivatives, and determined their minimum inhibitory concentrations (MICs) against Pseudomonas aeruginosa, their minimum hemolytic concentrations (MHCs) for human erythrocytes, and the Therapeutic Index (MHC/MIC ratio). We selected the derivative peptide K11, which had the highest therapeutic index (320) among the tested peptides, to determine the MICs against Gram-positive and Gram-negative bacteria and 22 clinical isolates including Acinetobacter baumannii, methicillin-resistant Staphylococcus aureus, Pseudomonas aeruginosa, Staphylococcus epidermidis, and Klebsiella pneumonia. K11 exhibited low MICs (less than 10 μg/mL) and broad-spectrum antimicrobial activity, especially against clinically isolated drug-resistant pathogens. Therefore, these results indicate that K11 is a promising candidate antimicrobial peptide for further studies

Guidance Law Design for a Class of Dual-Spin Mortars

To minimize the cost and maximize the ease of use, a class of dual-spin mortars is designed which only rely on GPS receiver and geomagnetic measurements. However, there are some problems to be solved when the range is small, such as low correction authority and trajectory bending. Guidance law design for this mortar is detailed. Different guidance laws were designed for the ascending and descending segments, respectively. By taking variable parameter guidance law in the vertical plane and using compensation in the lateral plane, the problems mentioned above were resolved. Roll angle resolving algorithms with geomagnetic measurements were demonstrated and the experiment results proved to be effective. In order to verify the effectiveness, Seven-Degrees-of-Freedom (7-DOF) rigid ballistic model were established and hardware in the loop simulation was introduced. After the transform function of the actuator was obtained, the control model of the shell was improved. The results of the Monte Carlo simulation demonstrate that the guidance law is suitable and the mortar can be effectively controlled

BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection

Temporal action detection (TAD) is extensively studied in the video understanding community by generally following the object detection pipeline in images. However, complex designs are not uncommon in TAD, such as two-stream feature extraction, multi-stage training, complex temporal modeling, and global context fusion. In this paper, we do not aim to introduce any novel technique for TAD. Instead, we study a simple, straightforward, yet must-known baseline given the current status of complex design and low detection efficiency in TAD. In our simple baseline (termed BasicTAD), we decompose the TAD pipeline into several essential components: data sampling, backbone design, neck construction, and detection head. We extensively investigate the existing techniques in each component for this baseline, and more importantly, perform end-to-end training over the entire pipeline thanks to the simplicity of design. As a result, this simple BasicTAD yields an astounding and real-time RGB-Only baseline very close to the state-of-the-art methods with two-stream inputs. In addition, we further improve the BasicTAD by preserving more temporal and spatial information in network representation (termed as PlusTAD). Empirical results demonstrate that our PlusTAD is very efficient and significantly outperforms the previous methods on the datasets of THUMOS14 and FineAction. Meanwhile, we also perform in-depth visualization and error analysis on our proposed method and try to provide more insights on the TAD problem. Our approach can serve as a strong baseline for future TAD research. The code and model will be released at https://github.com/MCG-NJU/BasicTAD.Comment: Accepted by CVI