Project description:The functional diversity of soil microbial communities was explored for a poplar plantation, which was treated solely with biogas slurry, or combined with biochar at different fertilization intensities over several years.
Project description:The use of biofertilizers is becoming an economical and environmentally friendly alternative to promote sustainable agriculture. Biochar from microalgae can be applied to enhance the productivity of food crops through soil improvement, slow nutrient absorption and release, increased water uptake, and long-term mitigation of greenhouse gas sequestration. Therefore, the aim of this study was to evaluate the stimulatory effects of biochar produced from Spirulina platensis biomass on the development and seed production of rice plants. Biochar was produced by slow pyrolysis at 300°C, and characterization was performed through microscopy, chemical, and structural composition analyses. Molecular and physiological analyses were performed in rice plants submitted to different biochar concentrations (0.02, 0.1, and 0.5 mg mL-1) to assess growth and productivity parameters. Morphological and physicochemical characterization revealed a heterogeneous morphology and the presence of K and Mg minerals in the biochar composition. Chemical modification of compounds post-pyrolysis and a highly porous structure with micropores were observed. Rice plants submitted to 0.5 mg mL-1 of biochar presented a decrease in root length, followed by an increase in root dry weight. The same concentration influenced seed production, with an increase of 44% in the number of seeds per plant, 17% in the percentage of full seeds per plant, 12% in the weight of 1,000 full seeds, 53% in the seed weight per plant, and 12% in grain area. Differential proteomic analyses in shoots and roots of rice plants submitted to 0.5 mg mL-1 of biochar for 20 days revealed a fine-tuning of resource allocation towards seed production. These results suggest that biochar derived from Spirulina platensis biomass can stimulate rice seed production.
Project description:Embedding is the key step in single-cell Hi-C (scHi-C) analysis which relies on capturing biological meaningful heterogeneity at various levels of genome architecture. To understand the strength and limitations of existing tools in various applications, here we use ten scHi-C datasets to benchmark thirteen embedding tools including Va3DE, a new convolutional neural network model that can accommodate large cell numbers. We built a software framework to decouple the preprocessing options of existing tools and found that no single tool works best across all datasets under default settings. The difficulty levels and preferred resolutions are different between benchmark datasets, and the choice of data representation and preprocessing strongly impact the embedding performance. Embedding cells from early embryonic stages relies on long-range compartment-scale contacts, but resolving cell cycle phases and complex tissue requires short-range loop-scale contacts. Both random-walk and inverse document frequency (IDF) transformation prefers long-range “compartment-scale” over short-range “loop-scale” embedding, while deep-learning methods better overcome sparsity at both scales and are more versatile with different resolutions. Finally, “diagonal integration” with independent data modal is a promising approach to distinguish similar cell subpopulations. Our findings underscore the significance of appropriate priors for scHi-C embedding and offer new insights into genome architecture heterogeneity.