Mathematical Modeling of Cytotoxic Lymphocyte-Mediated Immune Response to Hepatitis B Virus Infection

Nowak's model of the human immunodeficiency virus (HIV) infection has been extensively and successfully used to simulate the interaction between HIV and cytotoxic lymphocyte- (CTL-) mediated immune response. However, this model is not available for hepatitis B virus (HBV) infection. As the enhanced recruitment of virus-specific CTLs into the liver has been an important novel concept in the pathogenesis of hepatitis B, we develop a specific mathematical model analyzing the relationship between HBV and the CTL-mediated immune response, and the indicator of the liver cell damage, alanine aminotransferase (ALT). The stability condition of the complete recovery equilibrium point at which HBV will be eliminated entirely from the body is discussed. A different set of parameters is used in the simulation, and the results show that the model can interpret the wide variety of clinical manifestations of HBV infection. The model suggests that a rapid and vigorous CTL response is required for resolution of HBV infection

High-Resolution Structure of the N-Terminal Endonuclease Domain of the Lassa Virus L Polymerase in Complex with Magnesium Ions

Lassa virus (LASV) causes deadly hemorrhagic fever disease for which there are no vaccines and limited treatments. LASV-encoded L polymerase is required for viral RNA replication and transcription. The functional domains of L–a large protein of 2218 amino acid residues–are largely undefined, except for the centrally located RNA-dependent RNA polymerase (RdRP) motif. Recent structural and functional analyses of the N-terminal region of the L protein from lymphocytic choriomeningitis virus (LCMV), which is in the same Arenaviridae family as LASV, have identified an endonuclease domain that presumably cleaves the cap structures of host mRNAs in order to initiate viral transcription. Here we present a high-resolution crystal structure of the N-terminal 173-aa region of the LASV L protein (LASV L173) in complex with magnesium ions at 1.72 Å. The structure is highly homologous to other known viral endonucleases of arena- (LCMV NL1), orthomyxo- (influenza virus PA), and bunyaviruses (La Crosse virus NL1). Although the catalytic residues (D89, E102 and K122) are highly conserved among the known viral endonucleases, LASV L endonuclease structure shows some notable differences. Our data collected from in vitro endonuclease assays and a reporter-based LASV minigenome transcriptional assay in mammalian cells confirm structural prediction of LASV L173 as an active endonuclease. The high-resolution structure of the LASV L endonuclease domain in complex with magnesium ions should aid the development of antivirals against lethal Lassa hemorrhagic fever

Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention

Recent large language models (LLMs) have revealed strong abilities to understand natural language. Since most of them share the same basic structure, i.e. the transformer block, possible contributors to their success in the training process are scaling and instruction tuning. However, how these factors affect the models' language perception is unclear. This work compares the self-attention of several existing LLMs (LLaMA, Alpaca and Vicuna) in different sizes (7B, 13B, 30B, 65B), together with eye saccade, an aspect of human reading attention, to assess the effect of scaling and instruction tuning on language perception. Results show that scaling enhances the human resemblance and improves the effective attention by reducing the trivial pattern reliance, while instruction tuning does not. However, instruction tuning significantly enhances the models' sensitivity to instructions. We also find that current LLMs are consistently closer to non-native than native speakers in attention, suggesting a sub-optimal language perception of all models. Our code and data used in the analysis is available on GitHub

Development of a novel detection technology for drug resistance mutation sites of Mycobacterium tuberculosis using Luminex liquid chip technology

Purpose: To develop a novel detection technology for drug-resistance mutation sites of Mycobacterium tuberculosis (MTB) using a Luminex liquid chip.Methods: Using polymerase chain reaction (PCR) amplification and hybridization analysis, MTB infection and drug-resistant mutation sites of the first-line and second-line anti-MTB drugs were simultaneously identified. A novel detection method was applied to analyze the wild-type standard strains of MTB and 33 clinical samples, and the results were compared with Sanger sequencing results for PCR products.Results: It was revealed that the sensitivity (100 %) and specificity (100 %) of the novel detection method for 31 samples were satisfactory, and all mutation sites were correctly detected. Compared with traditional PCR and culture-based drug sensitivity test, the novel detection method increased the speed of identification of drug-resistant TB, reduced clinicians' workload, and decreased treatment cost. Among 31 samples, 12.90 % were resistant to isoniazid (4/31), 35.48 % to rifampicin (11/31), and 12.90 % to ofloxacin (p < 0.05). Furthermore, 2 (6.45 %) samples were resistant to both isoniazid and rifampicin, 2 (6.45 %) samples to both rifampicin and ofloxacin, and 1 (3.22 %) sample to both isoniazid and ofloxacin, and 1 (3.22%) sample to all the three drugs (p < 0.05).Conclusion: Development and wide application of this novel detection method will facilitate the treatment of MTB, thus reducing the spread of drug-resistant MTB, and improving the prevention and treatment of MTB

A Detection Method of Rice Process Quality Based on the Color and BP Neural Network

Abstract. This paper proposed a detection method of rice process quality using the color and BP neural network. A rice process quality detection device based on computer vision technology was designed to get rice image, a circle of the radius R in the abdomen of the rice was determined as a color feature extraction area, and which was divided into five concentric sub-domains by the average area, the average color of each sub-region H was extraction as the color feature values described in the surface process quality of rice, and then the 5 color feature values as input values were imported to the BP neural network to detection the surface process quality of rice. The results show that the average accuracy of this method is 92.50% when it was used to detect 4 types of rice of different process quality

Blocking interaction between SHP2 and PD‐1 denotes a novel opportunity for developing PD‐1 inhibitors

Small molecular PD‐1 inhibitors are lacking in current immuno‐oncology clinic. PD‐1/PD‐L1 antibody inhibitors currently approved for clinical usage block interaction between PD‐L1 and PD‐1 to enhance cytotoxicity of CD8+ cytotoxic T lymphocyte (CTL). Whether other steps along the PD‐1 signaling pathway can be targeted remains to be determined. Here, we report that methylene blue (MB), an FDA‐approved chemical for treating methemoglobinemia, potently inhibits PD‐1 signaling. MB enhances the cytotoxicity, activation, cell proliferation, and cytokine‐secreting activity of CTL inhibited by PD‐1. Mechanistically, MB blocks interaction between Y248‐phosphorylated immunoreceptor tyrosine‐based switch motif (ITSM) of human PD‐1 and SHP2. MB enables activated CTL to shrink PD‐L1 expressing tumor allografts and autochthonous lung cancers in a transgenic mouse model. MB also effectively counteracts the PD‐1 signaling on human T cells isolated from peripheral blood of healthy donors. Thus, we identify an FDA‐approved chemical capable of potently inhibiting the function of PD‐1. Equally important, our work sheds light on a novel strategy to develop inhibitors targeting PD‐1 signaling axis

Dynamic Voxel Grid Optimization for High-Fidelity RGB-D Supervised Surface Reconstruction

Direct optimization of interpolated features on multi-resolution voxel grids has emerged as a more efficient alternative to MLP-like modules. However, this approach is constrained by higher memory expenses and limited representation capabilities. In this paper, we introduce a novel dynamic grid optimization method for high-fidelity 3D surface reconstruction that incorporates both RGB and depth observations. Rather than treating each voxel equally, we optimize the process by dynamically modifying the grid and assigning more finer-scale voxels to regions with higher complexity, allowing us to capture more intricate details. Furthermore, we develop a scheme to quantify the dynamic subdivision of voxel grid during optimization without requiring any priors. The proposed approach is able to generate high-quality 3D reconstructions with fine details on both synthetic and real-world data, while maintaining computational efficiency, which is substantially faster than the baseline method NeuralRGBD.Comment: For the project, see https://yanqingan.github.io

PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fields

Identifying spatially complete planar primitives from visual data is a crucial task in computer vision. Prior methods are largely restricted to either 2D segment recovery or simplifying 3D structures, even with extensive plane annotations. We present PlanarNeRF, a novel framework capable of detecting dense 3D planes through online learning. Drawing upon the neural field representation, PlanarNeRF brings three major contributions. First, it enhances 3D plane detection with concurrent appearance and geometry knowledge. Second, a lightweight plane fitting module is proposed to estimate plane parameters. Third, a novel global memory bank structure with an update mechanism is introduced, ensuring consistent cross-frame correspondence. The flexible architecture of PlanarNeRF allows it to function in both 2D-supervised and self-supervised solutions, in each of which it can effectively learn from sparse training signals, significantly improving training efficiency. Through extensive experiments, we demonstrate the effectiveness of PlanarNeRF in various scenarios and remarkable improvement over existing works