3DCFS : Fast and robust joint 3D semantic-instance segmentation via coupled feature selection

We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost

High-efficient screening method for identification of key genes in breast cancer through microarray and bioinformatics

Background/Aim: The aim of the present study was to identify key pathways and genes in breast cancer and develop a new method for screening key genes with abnormal expression based on bioinformatics. Materials and Methods: Three microarray datasets GSE21422, GSE42568 and GSE45827 were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were analyzed using GEO2R. The gene ontology (GO) and pathway enrichment analysis were established through DAVID database. The protein–protein interaction (PPI) network was performed through the Search Tool for the Retrieval of Interacting Genes (STRING) database and managed by Cytoscape. The overall survival (OS) analysis of the 4 genes including AURKA, CDH1, CDK1 and PPARG that had higher degrees in this network was uncovered Kaplan-Meier analysis. Results: A total of 811 DEGs were identified in breast cancer, which were enriched in biological processes, including cell cycle, mitosis, vessel development and lipid metabolic. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the up-regulated DEGs were particularly involved in cell cycle, progesterone-mediated oocyte maturation and leukocyte transendothelial migration, while the down-regulated DEGs were mainly involved in regulation of lipolysis, fatty acid degradation and glycerolipid metabolism. Through PPI network analysis, 14 hub genes were identified. Among them, the high expression of AURKA, CDH1 and CDK1 were associated with worse OS of breast cancer patients; while the high expression of PPARG was linked with better OS. Conclusion: The present study identified key pathways and genes involved in breast cancer which are potential molecular targets for breast cancer treatment and diagnosis

Implications of MDCK cell heterogeneity in cell-based influenza vaccine production

Influenza is a global public health issue that causes serious illness with high mortality rate. Currently, Madin-Darby canine kidney (MDCK) cell culture-based influenza vaccine production moving up to the front as an inexorable trend for the substitution of egg-based vaccine production, owing to its high degree of flexibility and scalability. However, MDCK cells are a continuous cell line and comprise a heterogeneous pool of non-clonal cells that differ in morphological as well as functional features in influenza virus production. The impurity of cell population may lead to fugacious tendency in virus production, and long-term culture may bring potential risk of unstable viral production or vaccine quality as cells in MDCK subclonal population may encounter unexpected manifestation of chromosomal rearrangement, loss of the virus susceptibility, or reduction of the virus partials packaging capability during the culture. Although many details of the influenza virus life cycle have already been unraveled, little is known about the ability of subclones in virus infection, intracellular replication, and virus release during viral vaccine production process. With the widely utilizing of omics-based approaches and progressively accumulating of omics database, transcriptome profile analysis will be a powerful strategy to explore the mechanism of cell heterogeneity, providing great significance for the development of robust virus producing cell line and robust virus production process. This work aims to explore a deeper understanding on the MDCK cell heterogeneity used in influenza virus production. For this purpose, a MDCK cell line that has been extensively used in industrial production was subcloned and examined for the influenza virus productivity. The virus productivity spread over a wide range of more than 300-fold among different clones, which revealed large variations in their ability to produce progeny viruses. The high and low producer as well as parent cell population were expanded to explore the intracellular virus propagation process, and the expression levels of all the annotated genes were quantified across the different subclones using RNA-seq. The RT-qPCR results showed that the influenza virus RNA synthesis and virus release differed dramatically among subclones during a synchronized single-cycle infection. Pathway analysis performed on the genes indicated that most of the genes are not differentially expressed, but a few key cellular metabolic pathways are differentially expressed among the subclones, especially the genes related to the virus infection, replication and release. These results spurs further hypothesis to improve our mechanistic understanding of cell line stability and virus propagation process, which will have significant impact on rationalizing cell line development of viral vaccine producing mammalian cells

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling

Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data. Many algorithms are designed for online time series forecasting, with some exploiting cross-variable dependency while others assume independence among variables. Given every data assumption has its own pros and cons in online time series modeling, we propose \textbf{On}line \textbf{e}nsembling \textbf{Net}work (OneNet). It dynamically updates and combines two models, with one focusing on modeling the dependency across the time dimension and the other on cross-variate dependency. Our method incorporates a reinforcement learning-based approach into the traditional online convex programming framework, allowing for the linear combination of the two models with dynamically adjusted weights. OneNet addresses the main shortcoming of classical online learning methods that tend to be slow in adapting to the concept drift. Empirical results show that OneNet reduces online forecasting error by more than $\mathbf{50\%}$ compared to the State-Of-The-Art (SOTA) method. The code is available at \url{https://github.com/yfzhang114/OneNet}.Comment: 32 pages, 11 figures, 37th Conference on Neural Information Processing Systems (NeurIPS 2023