Re-annotation improved large-scale assembly of the reef-building coral Acropora intermedia.

Acropora corals, primary reef-builders providing habitat for numerous marine species, now face novel survival pressures due to environmental changes. Acropora intermedia (Brook 1891), a significant contributor to the vibrant ecosystems of coral reefs in the Indo-Pacific Ocean, also exhibits enhanced resistance to both thermal and acid stress. To advance future studies, we report an improved high-quality genome assembly for A.

Complete mitochondrial genome and phylogenetic analysis of (Pallas, 1766).

The coral genus This study presents an updated complete mitochondrial genome of (Pallas, 1766). The final mitogenome spans 17,009 bp in length and contains 13 protein-coding genes, two transfer RNA genes, and two ribosomal RNA genes. It is worth noting that a previously published misannotation of the cytochrome c oxidase subunit I () gene has been corrected.

High-quality genome assembly of the azooxanthellate coral Tubastraea coccinea (Lesson, 1829).

Coral reefs are among the most biodiverse and economically significant ecosystems globally, yet they are increasingly degrading due to global climate change and local human activities. The sun coral Tubastraea coccinea (T. coccinea) an obligate heterotroph lacking symbiotic zooxanthellae, exhibits remarkable tolerance to conditions that cause bleaching and mortality in zooxanthellate species.

Elucidating the role of liver enzymes as markers and regulators in ovarian cancer: a synergistic approach using Mendelian randomization, single-cell analysis, and clinical evidence.

Objective: To investigate the association between liver enzymes and ovarian cancer (OC), and to validate their potential as biomarkers and their mechanisms in OC. Methods Genome-wide association studies for OC and levels of enzymes such as Alkaline phosphatase (ALP), Aspartate aminotransferase (AST), Alanine aminotransferase, and gamma-glutamyltransferase were analyzed. Univariate and multivariate Mendelian randomization (MR), complemented by the Steiger test, identified enzymes with a potential causal relationship to OC.

Semi-Automatic Prostate Segmentation From Ultrasound Images Using Machine Learning and Principal Curve Based on Interpretable Mathematical Model Expression.

Accurate prostate segmentation in transrectal ultrasound (TRUS) is a challenging problem due to the low contrast of TRUS images and the presence of imaging artifacts such as speckle and shadow regions. To address this issue, we propose a semi-automatic model termed Hybrid Segmentation Model (H-SegMod) for prostate Region of Interest (ROI) segmentation in TRUS images. H-SegMod contains two cascaded stages.