The Inhibiting Effects of High-Dose Biochar Application on Soil Microbial Metagenomics and Rice (Oryza sativa 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 (Oryza sativa L.) production and soil biological characteristics have not been fully studied. In this greenhouse pot experiment, combined with soil microbial metagenomics, three treatments in triplicates were conducted to explore the responses of rice production, soil chemical properties, and soil biological properties to high-dose applications of biochar (5%, w/w) prepared using peanut waste (peanut hulls and straw). The results show that peanut hulls, with a loose texture and pore structure, are a raw material with stronger effects for preparing biochar than peanut straw in terms of its physical structure. In a rice monoculture system, high-dose applications of biochar (5%, w/w) can slightly increase the grains per spike, while significantly inhibiting the spike number per pot and the percentage of setting. High-dose applications of biochar also have significant negative effects on the diversity and stability of soil bacterial and archaeal communities. Moreover, the microbial metabolism and nutrient cycling processes are also significantly affected by changing the soil carbon/nitrogen ratio. This study discusses the response mechanisms of rice production and soil biology to high-dose biochar applications, and complements the understanding of irrational biochar application on agricultural production and land sustainability.

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

Rhododendron pulchrum 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. Results show that a total of 35.55 to 40.56 million high-quality clean reads were obtained, of which 28.56 to 32.65 million reads were mapped to the reference genome. For their annotation, 28,273, 18,054, 24,301, 19,099, and 11,507 genes were allocated to Nr, Swiss-Prot, Pfam, GO, and KEGG databases, correspondingly. There were differentially expressed genes among the four different petal samples, including signal-transduction-related genes, anthocyanin biosynthesis genes, and transcription factors. We found that the higher expressed levels of genes associated with flavonol synthase (FLS) might be the key to white formation, and the formation of red color may be related to the higher expression of flavanone 4-reductase (DFR) families. Overall, our study provides some valuable information for exploring and understanding the flower color intensity variation in R. pulchrum.

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.1,2,3,4,5,6,7,8,9,10 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.11 Cuttlefish dynamically camouflage to their surroundings by changing the color, pattern, and texture of their skin. Camouflage is optically driven and is achieved by expanding and contracting hundreds of thousands of pigment-filled saccules (chromatophores) in the skin, which are controlled by motor neurons emanating from the brain. We generated a dwarf cuttlefish brain atlas using magnetic resonance imaging (MRI), deep learning, and histology, and we built an interactive web tool (https://www.cuttlebase.org/) to host the data. Guided by observations in other cephalopods,12,13,14,15,16,17,18,19,20 we identified 32 brain lobes, including two large optic lobes (75% the total volume of the brain), chromatophore lobes whose motor neurons directly innervate the chromatophores of the color-changing skin, and a vertical lobe that has been implicated in learning and memory. The brain largely conforms to the anatomy observed in other Sepia species and provides a valuable tool for exploring the neural basis of behavior in the experimentally facile dwarf cuttlefish.

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. With a 97% sensitivity, 93% specificity, 91% Jaccard similarity, and 95% Dice coefficient, our proposed method called Residual Attention U-Net with edge-enhancement surpassed the state-of-the-art U-Net in segmentation performance as evaluated by the traditional metrics. More remarkably, the same proposed candidate also performed the best in terms of the preservation of valuable shape features, namely area, eccentricity, major axis length, solidity and orientation. These improvements on shape feature preservation can serve as useful assets for downstream cell tracking and quantification of changes in cell statistics or features over time.

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. The model was first trained, optimized, and validated in mice, and was then transferred and adapted to humans. We observe that the model can substitute for Gadolinium-based contrast agents in approximating cerebral blood volume, a quantitative representation of brain activity, at sub-millimeter granularity. Furthermore, we validate the use of our deep-learned prediction maps to identify functional abnormalities in the aging brain using locally obtained MRI scans, and in the brain of patients with Alzheimer's disease using publicly available MRI scans from ADNI. Since it is derived from a commonly-acquired MRI protocol, this framework has the potential for broad clinical utility and can also be applied retrospectively to research scans across a host of neurological/functional diseases.