Sparse Dense Fusion for 3D Object Detection

With the prevalence of multimodal learning, camera-LiDAR fusion has gained popularity in 3D object detection. Although multiple fusion approaches have been proposed, they can be classified into either sparse-only or dense-only fashion based on the feature representation in the fusion module. In this paper, we analyze them in a common taxonomy and thereafter observe two challenges: 1) sparse-only solutions preserve 3D geometric prior and yet lose rich semantic information from the camera, and 2) dense-only alternatives retain the semantic continuity but miss the accurate geometric information from LiDAR. By analyzing these two formulations, we conclude that the information loss is inevitable due to their design scheme. To compensate for the information loss in either manner, we propose Sparse Dense Fusion (SDF), a complementary framework that incorporates both sparse-fusion and dense-fusion modules via the Transformer architecture. Such a simple yet effective sparse-dense fusion structure enriches semantic texture and exploits spatial structure information simultaneously. Through our SDF strategy, we assemble two popular methods with moderate performance and outperform baseline by 4.3% in mAP and 2.5% in NDS, ranking first on the nuScenes benchmark. Extensive ablations demonstrate the effectiveness of our method and empirically align our analysis

Identification of Differentially Expressed Hub Genes Associated With Immune Cell Recruitment in Claudin-Low Breast Cancer

Breast cancer (BCa) is the most common malignancy in women and claudin-low breast cancer (CL-BCa) is a newly identified BCa subtype characterized by low expression of claudin 3&4&7. However, the hub genes associated with the recruitment of immune cells into CL-BCa were rarely described. This study aimed at exploring the differentially expressed hub genes associated with tumor-infiltrating immune cells in CL-BCa by a multi-approach bioinformatics analysis. The top 200 genes associated with CL-BCa were screened in the METABRIC dataset; the PPI network was constructed using STRING and Cytoscape; tumor-infiltrating immune cells were analyzed by TIMER 2.0; and the correlation of feature cytokines and claudins on survival was examined in METABRIC and TCGA datasets. Consequently, we found that the fraction of tumor-infiltrating immune cells, especially CD8+T cells and macrophages, increased in the CL-BCa. Differentially expressed cytokines (CCL5, CCL19, CXCL9 and CXCL10) were related to the overall survival, and their expression levels were also examined both in tumor tissues of CL-BCa patients by IHC and in typical CL-BCa cell lines by qPCR. Moreover, the BCa patients with low expression of these differentially expressed claudins (CLDN8, CLDN11 and CLDN19) showed a worse overall survival. This study sheds light on molecular features of CL-BCa on immune microenvironments and contributes to identification of prognosis biomarkers for the CL-BCa patients

Association of ATM Gene Polymorphism with PTC Metastasis in Female Patients

Ataxia telangiectasia mutated (ATM) gene is critical in the process of recognizing and repairing DNA lesions and is related to invasion and metastasis of malignancy. The incidence rate of papillary thyroid cancer (PTC) has increased for several decades and is higher in females than males. In this study, we want to investigate whether ATM polymorphisms are associated with gender-specific metastasis of PTC. 358 PTC patients in Northern China, including 109 males and 249 females, were included in our study. Four ATM single nucleotide polymorphisms (SNPs) were genotyped using Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS). Association between genotypes and the gender-specific risk of metastasis was assessed by odds ratios (OR) and 95% confidence intervals (CI) under the unconditional logistic regression analysis. Significant associations were observed between rs189037 and metastasis of PTC in females under different models of inheritance (codominant model: OR=0.15, 95% CI 0.04–0.56, P=0.01 for GA versus GG and OR=0.08, 95% CI 0.01–0.74, P=0.03 for AA versus GG, resp.; dominant model: OR=0.49, 95% CI 0.25–0.98, P=0.04; overdominant model: OR=0.47, 95% CI 0.25–0.89, P=0.02). However, no association remained significant after Bonferroni correction. Our findings suggest a possible association between ATM rs189037 polymorphisms and metastasis in female PTCs

Insights into the genetic influences of the microbiota on the life span of a host

Escherichia coli (E. coli) mutant strains have been reported to extend the life span of Caenorhabditis elegans (C. elegans). However, the specific mechanisms through which the genes and pathways affect aging are not yet clear. In this study, we fed Drosophila melanogaster (fruit fly) various E. coli single-gene knockout strains to screen mutant strains with an extended lifespan. The results showed that D. melanogaster fed with E. coli purE had the longest mean lifespan, which was verified by C. elegans. We conducted RNA-sequencing and analysis of C. elegans fed with E. coli purE (a single-gene knockout mutant) to further explore the underlying molecular mechanism. We used differential gene expression (DGE) analysis, enrichment analysis, and gene set enrichment analysis (GSEA) to screen vital genes and modules with significant changes in overall expression. Our results suggest that E. coli mutant strains may affect the host lifespan by regulating the protein synthesis rate (cfz-2) and ATP level (catp-4). To conclude, our study could provide new insights into the genetic influences of the microbiota on the life span of a host and a basis for developing anti-aging probiotics and drugs