Gradient-Index Optics for Laser Applications

Faculty advisor: James LegerThis research was supported by the Undergraduate Research Opportunities Program (UROP)

Generalizable Neural Voxels for Fast Human Radiance Fields

Rendering moving human bodies at free viewpoints only from a monocular video is quite a challenging problem. The information is too sparse to model complicated human body structures and motions from both view and pose dimensions. Neural radiance fields (NeRF) have shown great power in novel view synthesis and have been applied to human body rendering. However, most current NeRF-based methods bear huge costs for both training and rendering, which impedes the wide applications in real-life scenarios. In this paper, we propose a rendering framework that can learn moving human body structures extremely quickly from a monocular video. The framework is built by integrating both neural fields and neural voxels. Especially, a set of generalizable neural voxels are constructed. With pretrained on various human bodies, these general voxels represent a basic skeleton and can provide strong geometric priors. For the fine-tuning process, individual voxels are constructed for learning differential textures, complementary to general voxels. Thus learning a novel body can be further accelerated, taking only a few minutes. Our method shows significantly higher training efficiency compared with previous methods, while maintaining similar rendering quality. The project page is at https://taoranyi.com/gneuvox .Comment: Project page: http://taoranyi.com/gneuvo

Anticancer effects of 7,8-dihydromethysticin in human leukemia cells are mediated via cell-cycle dysregulation, inhibition of cell migration and invasion and targeting JAK/STAT pathway

The main focus of this research work was to study the anticancer properties of 7,8-dihydromethysticin against HL-60 leukemia cells. Investigations were also performed to check its impact on the phases of the cell cycle, cell migration and invasion, JAK/STAT signalling pathway and intracellular mitochondrial membrane potential (MMP) and reactive oxygen species (ROS). Cell proliferation was assessed through 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay and effects on colony formation were examined via clonogenic assay. Flow cytometry and western blott analysis were performed to investigate the distribution of cell cycle phases. Flow cytometric analysis was performed for the examination of MMP and ROS production. The effect on JAK/STAT signalling pathway was examined through western blot analysis. Results depicted that 7,8-dihydromethysticin induced concentration- as well as time-dependent inhibition of cell proliferation in leukemia HL-60 cells. Clonogenic assay indicated potential suppression in leukemia HL-60 cell colonies. The 7,8-dihydromethysticin molecule also caused cell cycle arrest at G2/M-phase along with concentration-dependent inhibition of cyclin B1, D1 and E. ROS and MMP measurements indicated significant ROS enhancement and MMP suppression with increasing 7,8-dihydromethysticin concentrations. Additionally, 7,8-dihydromethysticin led to remarkable dose-reliant inhibition of cell invasion as well as cell migration. Therefore, 7,8-dihydromethysticin should be considered a valuable candidate for leukemia research and chemoprevention

Prompt Tuning for Graph Neural Networks

In recent years, prompt tuning has set off a research boom in the adaptation of pre-trained models. In this paper, we propose Graph Prompt as an efficient and effective alternative to full fine-tuning for adapting the pre-trianed GNN models to downstream tasks. To the best of our knowledge, we are the first to explore the effectiveness of prompt tuning on existing pre-trained GNN models. Specifically, without tuning the parameters of the pre-trained GNN model, we train a task-specific graph prompt that provides graph-level transformations on the downstream graphs during the adaptation stage. Then, we introduce a concrete implementation of the graph prompt, called GP-Feature (GPF), which adds learnable perturbations to the feature space of the downstream graph. GPF has a strong expressive ability that it can modify both the node features and the graph structure implicitly. Accordingly, we demonstrate that GPF can achieve the approximately equivalent effect of any graph-level transformations under most existing pre-trained GNN models. We validate the effectiveness of GPF on numerous pre-trained GNN models, and the experimental results show that with a small amount (about 0.1% of that for fine-tuning ) of tunable parameters, GPF can achieve comparable performances as fine-tuning, and even obtain significant performance gains in some cases

ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction

For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of stock market predictions. Diverging from conventional methods, we pioneer an approach that integrates sentiment analysis, macroeconomic indicators, search engine data, and historical prices within a multi-attention deep learning model, masterfully decoding the complex patterns inherent in the data. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility

DropMessage: Unifying Random Dropping for Graph Neural Networks

Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also faces some challenges, such as over-fitting, over-smoothing, and non-robustness. Previous works indicate that these problems can be alleviated by random dropping methods, which integrate noises into models by randomly masking parts of the input. However, some open-ended problems of random dropping on GNNs remain to solve. First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models. Second, random noises introduced to GNNs cause the incomplete coverage of parameters and unstable training process. In this paper, we propose a novel random dropping method called DropMessage, which performs dropping operations directly on the message matrix and can be applied to any message-passing GNNs. Furthermore, we elaborate the superiority of DropMessage: it stabilizes the training process by reducing sample variance; it keeps information diversity from the perspective of information theory, which makes it a theoretical upper bound of other methods. Also, we unify existing random dropping methods into our framework and analyze their effects on GNNs. To evaluate our proposed method, we conduct experiments that aims for multiple tasks on five public datasets and two industrial datasets with various backbone models. The experimental results show that DropMessage has both advantages of effectiveness and generalization

A real-world study of antifibrotic drugs-related adverse events based on the United States food and drug administration adverse event reporting system and VigiAccess databases

Objectives: This study aims to investigate adverse events (AEs) and adverse drug reactions (ADRs) associated with pirfenidone and nintedanib, two antifibrotic drugs used to treat idiopathic pulmonary fibrosis (IPF).Methods: Reporting odds ratio (ROR) and proportional reporting ratio (PRR) analyses were conducted to assess the association between these drugs and signals at both the preferred term (PT) and system organ class (SOC) levels.Results: 55,949 reports for pirfenidone and 35,884 reports for nintedanib were obtained from the FAERS database. The VigiAccess database provided 37,187 reports for pirfenidone and 23,134 reports for nintedanib. Male patients and individuals over the age of 65 were more likely to report AEs. Gastrointestinal disorders emerged as the most significant signal at SOC level for both drugs. Furthermore, nausea, diarrhoea, and decreased appetite were observed at the PT level. We further identified notable signals, including hemiplegic migraine for pirfenidone and asthenia, constipation, and flatulence for nintedanib, which were previously unknown or underestimated ADRs.Conclusion: This study has identified AEs and ADRs associated with pirfenidone and nintedanib, confirming that the majority of the corresponding label information indicates relative safety. However, it is essential to take unexpected risk signals seriously, necessitating further research to manage the safety profiles of these drugs

Changes on the conformational and functional properties of soybean protein isolate induced by quercetin

The conformational changes and functional properties of SPI induced by quercetin was investigated via fourier transform infrared (FTIR) spectroscopy, fluorescence spectroscopy, circular dichroism (CD) spectroscopy and molecular docking. A decrease in the fluorescence intensity and a blue shift in the maximum wavelength were observed due to the binding process with fluorescent residues. The analysis of Stern-Volmer equation showed that the fluorescence quenching induced by quercetin took the form of static quenching, and the binding stoichiometry between SPI and quercetin was 1:1. The values of ΔH and ΔS were both positive illustrating that hydrophobic interaction was the primary binding force between quercetin and SPI. Results of FTIR and CD indicated that the binding with quercetin changed the secondary structure of SPI, resulting in a partially unfolded and more flexible structure. SDS-PAGE confirmed there was no covalent interaction between the two constituents. Molecular docking demonstrated that there were stable configurations and high matching degrees in both 11S and 7S proteins with quercetin via hydrogen bonds and hydrophobic interactions. Meanwhile, modification by quercetin enhanced the foaming and emulsifying capacities of SPI. These findings might provide theory reference for elucidation the mechanism of polyphenols-proteins interaction and development of related food additive products in future