Development of a web-based high-throughput marker design program: CAPS (cleaved amplified polymorphic sequence) Maker.

Background: Genetic markers are crucial for breeding crops with desired agronomic traits, and their development can be expedited using next-generation sequencing (NGS) and bioinformatics tools. Numerous tools have been developed to design molecular markers, enhancing the convenience, accuracy, and efficiency of molecular breeding. However, these tools primarily focus on genetic variants within short user-input sequences, despite the availability of extensive omics data for genomic variants.

Education level, contraceptive communication, and IUD use among rural-to-urban migrants in China: A qualitative study.

Chinese rural-to-urban migrant workers have high rates of unintended pregnancy, yet many are reluctant to choose the most effective forms of contraception, such as IUDs (intrauterine devices). Those who do are often socioeconomically disadvantaged, a finding that contradicts much health research, namely that higher SES individuals can access better healthcare. This puzzle highlights the need to understand better migrant workers' contraceptive decision-making.

Digital mapping of resected cancer specimens: The visual pathology report.

Background: The current standard-of-care pathology report relies only on lengthy written text descriptions without a visual representation of the resected cancer specimen. This study demonstrates the feasibility of incorporating virtual, three-dimensional (3D) visual pathology reports to improve communication of final pathology reporting.

Materials And Methods: Surgical specimens are 3D scanned and virtually mapped alongside the pathology team to replicate grossing.

Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset.

This study conducts a rigorous comparative analysis between two cutting-edge instance segmentation methods, Mask R-CNN and YOLOv8, focusing on stomata pore analysis. A novel dataset specifically tailored for stomata pore instance segmentation, named PhenomicsStomata, was introduced. This dataset posed challenges such as low resolution and image imperfections, prompting the application of advanced preprocessing techniques, including image enhancement using the Lucy-Richardson Algorithm.

Uncertainty Quantification in Automated Detection of Vertebral Metastasis Using Ensemble Monte Carlo Dropout.

The accurate and early detection of vertebral metastases is crucial for improving patient outcomes. Although deep-learning models have shown potential in this area, their lack of prediction reliability and robustness limits their clinical utility. To address these challenges, we propose a novel technique called Ensemble Monte Carlo Dropout (EMCD) for uncertainty quantification (UQ), which combines the Monte Carlo dropout and deep ensembles.