Regulating Leaf Photosynthesis and Soil Microorganisms through Controlled-Release Nitrogen Fertilizer Can Effectively Alleviate the Stress of Elevated Ambient Ozone on Winter Wheat.

The mitigation mechanisms of a kind of controlled-release nitrogen fertilizer (sulfur-coated controlled-release nitrogen fertilizer, SCNF) in response to O stress on a winter wheat ( L.) variety (Nongmai-88) were studied in crop physiology and soil biology through the ozone-free-air controlled enrichment (O-FACE) simulation platform and soil microbial metagenomics. The results showed that SCNF could not delay the O-induced leaf senescence of winter wheat but could enhance the leaf size and photosynthetic function of flag leaves, increase the accumulation of nutrient elements, and lay the foundation for yield by regulating the release rate of nitrogen (N).

Transcriptomic and Hormonal Changes in Wheat Roots Enhance Growth under Moderate Soil Drying.

Understanding the mechanisms that regulate plant root growth under soil drying is an important challenge in root biology. We observed that moderate soil drying promotes wheat root growth. To understand whether metabolic and hormonic changes are involved in this regulation, we performed transcriptome sequencing on wheat roots under well-watered and moderate soil drying conditions.

The Inhibiting Effects of High-Dose Biochar Application on Soil Microbial Metagenomics and Rice ( L.) Production.

Biochar is usually considered as an organic improver which can improve soil and increase crop yields. However, the unrestricted application of biochar to normal-fertility farmland will cause chemical stress on crops and affect agricultural production. At present, the effects and mechanisms of high-dose applications of biochar on rice ( L.

Transcriptomic Analysis Reveals the Regulatory Mechanism of Color Diversity in Sweet (Ericaceae).

Sweet is a traditional ornamental plant cultivated in China and presents a great variation in petal coloration. However, few studies have been performed to reveal the genes involved and the regulatory mechanism of flower color formation in this plant. In this study, to explore the underlying genetic basis of flower color formation, transcriptome analysis was performed by high-throughput sequencing techniques on four petal samples of different colors: purple, pink, light pink, and white.

A brain atlas for the camouflaging dwarf cuttlefish, Sepia bandensis.

The coleoid cephalopods (cuttlefish, octopus, and squid) are a group of soft-bodied marine mollusks that exhibit an array of interesting biological phenomena, including dynamic camouflage, complex social behaviors, prehensile regenerating arms, and large brains capable of learning, memory, and problem-solving. The dwarf cuttlefish, Sepia bandensis, is a promising model cephalopod species due to its small size, substantial egg production, short generation time, and dynamic social and camouflage behaviors. Cuttlefish dynamically camouflage to their surroundings by changing the color, pattern, and texture of their skin.

Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features.

The ability to extrapolate gene expression dynamics in living single cells requires robust cell segmentation, and one of the challenges is the amorphous or irregularly shaped cell boundaries. To address this issue, we modified the U-Net architecture to segment cells in fluorescence widefield microscopy images and quantitatively evaluated its performance. We also proposed a novel loss function approach that emphasizes the segmentation accuracy on cell boundaries and encourages shape feature preservation.

Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brains.

While MRI contrast agents such as those based on Gadolinium are needed for high-resolution mapping of brain metabolism, these contrast agents require intravenous administration, and there are rising concerns over their safety and invasiveness. Furthermore, non-contrast MRI scans are more commonly performed than those with contrast agents and are readily available for analysis in public databases such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). In this article, we hypothesize that a deep learning model, trained using quantitative steady-state contrast-enhanced structural MRI datasets, in mice and humans, can generate contrast-equivalent information from a single non-contrast MRI scan.