Author Correction: The flying spider-monkey tree fern genome provides insights into fern evolution and arborescence (Nature Plants, (2022), 8, 5, (500-512), 10.1038/s41477-022-01146-6)

Correction to: Nature Plantshttps://doi.org/10.1038/s41477-022-01146-6, published online 9 May 2022. In the version of the article initially published, Dipak Khadka, who collected the samples in Nepal, was thanked in the Acknowledgements instead of being listed as an author. His name and affiliation (GoldenGate International College, Tribhuvan University, Battisputali, Kathmandu, Nepal) have been added to the authorship in the HTML and PDF versions of the article

A Novel Split-Radix Fast Algorithm for 2-D Discrete Hartley Transform

International audienceThis paper presents a fast split-radix-(2×2)/(8×8) algorithm for computing the two-dimensional (2-D) discrete Hartley transform (DHT) of length N×N with N = q*2m, where q is an odd integer. The proposed algorithm decomposes an N×N DHT into one N/2×N/2 DHT and forty-eight N/8×N/8 DHTs. It achieves an efficient reduction on the number of arithmetic operations, data transfers and twiddle factors compared to the split-radix-(2×2)/(4×4) algorithm. Moreover, the characteristic of expression in simple matrices leads to an easy implementation of the algorithm. If implementing the above two algorithms with fully parallel structure in hardware, it seems that the proposed algorithm can decrease the area complexity compared to the split-radix-(2×2)/(4×4) algorithm, but requires a little more time complexity. An application of the proposed algorithm to 2-D medical image compression is also provided

Identification and validation of a 4-extracellular matrix gene signature associated with prognosis and immune infiltration in lung adenocarcinoma

Background: The extracellular matrix (ECM) plays a crucial role in the development and tumor microenvironment of lung adenocarcinoma (LUAD). This study aimed to establish a risk score of ECM-related genes in LUAD and explore the association between the risk score and patient survival as well as immune cell infiltration, somatic mutations, and therapy response. Methods: Gene expression data from The Cancer Genome Atlas (TGCA) and eight Gene Expression Omnibus (GEO) databases were used to analyze and identify differentially expressed genes (DEGs). Prognostic ECM-related genes were identified and utilized to formulate a prognostic signature. A nomogram was constructed using TCGA dataset and validated in two GEO datasets. Differences between high- and low-risk patients were analyzed for function enrichment, immune cell infiltration, somatic mutations, and therapy response. Finally, Quantitative real-time PCR (qRT-PCR) was used to detect the mRNA expression of DEGs in LUAD. Results: A risk score based on four ECM-related genes, ANOS1, CD36, COL11A1, and HMMR, was identified as an independent prognostic factor for overall survival (OS) compared to other clinical variables. Subsequently, a nomogram incorporating the risk score and TNM staging was developed using the TCGA dataset. Internal and external validation of the nomogram, conducted through calibration plots, C-index, time-dependent receiver operating characteristics (ROC), integrated discrimination improvement (IDI), and decision curve analyses (DCA), demonstrated the excellent discriminatory ability and clinical practicability of this nomogram. The risk score correlated with the distribution of function enrichment, immune cell infiltration, and immune checkpoint expression. More somatic mutations occurred in the high-risk group. The risk score also demonstrated a favorable ability to predict immunotherapy response and drug sensitivity. Conclusion: A novel signature based on four ECM-related genes is developed to help predict LUAD prognosis. This signature correlates with tumor immune microenvironment and can predict the response to different therapies in LUAD patients

Construction and validation of a novel prognostic model of neutrophil‑related genes signature of lung adenocarcinoma

Abstract Lung adenocarcinoma (LUAD) remains an incurable disease with a poor prognosis. This study aimed to explore neutrophil‑related genes (NRGs) and develop a prognostic signature for predicting the prognosis of LUAD. NRGs were obtained by intersecting modular genes identified by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data and the marker genes of neutrophils identified from single-cell RNA-sequencing(scRNA-seq) data. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were run to construct a prognostic signature, follow by delineation of risk groups, and external validation. Analyses of ESTIMAT, immune function, Tumor Immune Dysfunction and Exclusion (TIDE) scores, Immune cell Proportion Score (IPS), and immune checkpoint genes between high- and low-risk groups were performed, and then analyses of drug sensitivity to screen for sensitive anticancer drugs in high-risk groups. A total of 45 candidate NRGs were identified, of which PLTP, EREG, CD68, CD69, PLAUR, and CYP27A1 were considered to be significantly associated with prognosis in LUAD and were used to construct a prognostic signature. Correlation analysis showed significant differences in the immune landscape between high- and low-risk groups. In addition, our prognostic signature was important for predicting drug sensitivity in the high-risk group. Our study screened for NRGs in LUAD and constructed a novel and effective signature, revealing the immune landscape and providing more appropriate guidance protocols in LUAD treatment