Weighted Multi-Scale Image Matching Based on Harris-Sift Descriptor

According to the rotational invariance of Harris corner detectorand the robustness of Sift descriptor. An improved Harris-Sift corner descriptor was proposed. At first, the algorithm given multi-scale strategy to Harris corner, improved corner counting method and removed redundant points at the same time, then, the corner was directly applied to low-pass Gaussian filter image. Based on the histogram of Sift feature descriptor, generates a new 128-dimensional feature vector descriptor by multi-scale Gauss weighted.Through the above, Harris corner detectorand Sift descriptorwas normalizedin the scale layer and gradient features. The experiment results indicated that, the improved corner descriptorcomprised both advantage of Harris corner detection and Sift feature descriptor. The method reduced the computation time and the false match rate, which could be validly applied to the robotstereo vision matching andthree-dimensional reconstruction. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3429  

ARU2-Net: A Deep Learning Approach for Global-Scale Oceanic Eddy Detection

Ocean eddies have a significant impact on marine ecosystems and the climate because they transport essential substances in the ocean. Detection of ocean eddies has become one of the most active topics in physical ocean research. In recent years, research based on deep learning has mainly focused on regional oceans, with small and specific data and relatively general detection results. This study processes the global eddy by pixel-by-pixel classification and generates a global eddy classification map with a resolution of 720 × 1440, which expands the data volume and improves the generality of the data. Moreover, a high-precision attention residual U 2 -Net model, referred to as ARU 2 -Net, is proposed, which is suitable for mining eddy surface features from sea level anomaly (SLA) and sea surface temperature (SST) data in the global ocean. ARU 2 -Net integrates the convolutional block attention module (CBAM). The channel attention of the CBAM module is used to learn the correlation features between the SST and SLA dual channels; the spatial attention mechanism of the CBAM module is used to learn the importance of the spatial location of the eddy, focusing on the locally important regions, which further improves the detection ability of ARU 2 -Net for eddies, and helps ARU 2 -Net to better identify the eddy categories. Finally, we demonstrate the effectiveness of our approach on the global eddy dataset, achieving a test performance of 94.926%, significantly exceeding previous detection in some areas

Identification and validation of a novel CD8+ T cell-associated prognostic model based on ferroptosis in acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly aggressive cancer with great heterogeneity and variability in prognosis. Though European Leukemia Net (ELN) 2017 risk classification has been widely used, nearly half of patients were stratified to “intermediate” risk and requires more accurate classification via excavating biological features. As new evidence showed that CD8+ T cell can kill cancer cells through ferroptosis pathway. We firstly use CIBERSORT algorithm to divide AMLs into CD8+ high and CD8+ low T cell groups, then 2789 differentially expressed genes (DEGs) between groups were identified, of which 46 ferroptosis-related genes associated with CD8+ T cell were sorted out. GO, KEGG analysis and PPI network were conducted based on these 46 DEGs. By jointly using LASSO algorithm and Cox univariate regression, we generated a 6-gene prognostic signature comprising VEGFA, KLHL24, ATG3, EIF2AK4, IDH1 and HSPB1. Low-risk group shows a longer overall survival. We then validated the prognostic value of this 6-gene signature using two independent external datasets and patient sample collection dataset. We also proved that incorporation of the 6-gene signature obviously enhanced the accuracy of ELN risk classification. Finally, gene mutation analysis, drug sensitive prediction, GSEA and GSVA analysis were conducted between high-risk and low-risk AML patients. Collectively, our findings suggested that the prognostic signature based on CD8+ T cell-related ferroptosis genes can optimize the risk stratification and prognostic prediction of AML patients

Integrative genetic and multi-omics analysis reveals the interleukin-6 receptor’s role in recurrent spontaneous abortion

BackgroundRecurrent spontaneous abortion (RSA) significantly impacts women’s health, yet the underlying biological mechanisms remain poorly defined. Understanding the molecular contributors to RSA is crucial for developing targeted interventions.ObjectiveThis study aims to investigate the causal relationships between plasma proteins and RSA, focusing on the identification of potential therapeutic targets through multi-omic approaches.MethodsWe utilized two-sample Mendelian randomization (MR) analyses integrating genome-wide association study (GWAS) data for both plasma proteins and RSA. Proteomic data were sourced from the UK Biobank-Plasma Proteome Project and deCODE Health Study. We further validated our findings through both bulk and single-cell RNA sequencing of clinical specimens, alongside quantitative real-time polymerase chain reaction and immunohistochemistry. A phenome-wide association study was also conducted to assess the safety and broader implications of identified targets.ResultsOur analyses identified the interleukin 6 receptor (IL6R) as a key candidate, with elevated plasma levels correlating with increased RSA risk. Furthermore, IL6R was found to be upregulated in RSA-related endometrial and decidual tissues. The phenome-wide association study provided insights into potential side effects and additional therapeutic indications for IL6R.ConclusionIL6R upregulation is mechanistically implicated in the pathogenesis of RSA, establishing it as a validated causal biomarker and a potentially actionable therapeutic target. This study not only highlights the role of IL6R in RSA but also supports its development into a therapeutic strategy with a comprehensive safety profile