Process mechanisms of nanobubble technology enhanced hydrolytic acidification of anaerobic digestion of lignocellulosic biomass

This study explored the efficiency of CO2-, N2-, and H2- nanobubble treatment in anaerobic digestion (AD) of rice straw, with a focus on the processes and metabolic pathways of hydrolytic acidification, and revealed the underlying mechanisms. Mechanistic investigations revealed that nanobubbles, particularly CO2 nanobubbles, significantly increased the degradation of amorphous cellulose, resulting in higher levels of soluble carbohydrates (6.27 % – 11.13 %), VFAs (4.39 % – 24.50 %), and a remarkable cumulative H2 yield (74 – 94 times) Microbial community analysis indicated that the CO2 nanobubble promoted the growth of acidifying bacterial communities, such as Mobilitalea, unclassified_f_Lachnospiraceae, and Bacteroides. This indicates that the introduction of CO2 nanobubbles improved the total abundance of predicted functional enzymes were increased by 14 %, resulting in the production of more easily degradable intermediates. Based on the analysis of total methane production and kinetic analysis, it can be concluded that nanobubble addition enhanced methane production levels of 4.22 %−7.79 % with lower lag time (λ) (0.88–1.06 day) compared to the control group (1.09 day). The results also elucidated changes in relative enzymatic activities involved in the bioconversion of cellulose and hemicellulose during the hydrolysis stage with nanobubble treatment. This work is more beneficial for understanding the promoting effect and mechanism of nanobubbles on AD, facilitating the more precise application of nanobubble technology in the field of renewable energy

Sequencing depth affects targeted power and FDC for DMRs with |<i>β</i>|≥2.

The ‘depth factor’ is the relative ratio of the new dataset’s library size over that from the original dataset. It reflects the impact of enlarging or down-sampling the sequencing depth of pilot data. (A), (B) Targeted power and FDC under different sequencing depths, grouped by sample size. (C) Joint visualization of the mean targeted power and FDC, over various sequencing depths and sample sizes. N = 100 simulations are conducted. The average sequencing depths of ‘Input’ and ‘IP’ from the pilot data are 3.51X and 0.54X, respectively.</p

Targeted power and FDC stratified by mean input values for DMRs with |<i>β</i>|≥2.

Six strata are defined based on input count data quantiles: stratum 1 (0%, 10%), stratum 2 (10%, 30%), stratum 3 (30%, 50%), stratum 4 (50%, 70%), stratum 5 (70%, 90%), and stratum 6 (90%, 100%). A nominal FDR value of 0.05 is used to define significance. (A), (B) Mean targeted power and FDC along strata. Each line represents one sample size choice. (C), (D) Targeted power and FDC distributions in stratum 3, separated by sample size. (E), (F) Targeted power and FDC distributions with 5 replicates per group, stratified by mean input count values. N = 100 simulations are conducted.</p

Overview of <i>magpie</i>.

magpie provides power evaluation for differential m6A methylation analysis. It takes pilot MeRIP-seq data as the input. Based on the pilot data, it obtains candidate regions, estimates key parameters, and conducts real-data-based simulations for statistical power evaluation.</p

Simulation settings and additional results.

Recently, novel biotechnologies to quantify RNA modifications became an increasingly popular choice for researchers who study epitranscriptome. When studying RNA methylations such as N6-methyladenosine (m6A), researchers need to make several decisions in its experimental design, especially the sample size and a proper statistical power. Due to the complexity and high-throughput nature of m6A sequencing measurements, methods for power calculation and study design are still currently unavailable. In this work, we propose a statistical power assessment tool, magpie, for power calculation and experimental design for epitranscriptome studies using m6A sequencing data. Our simulation-based power assessment tool will borrow information from real pilot data, and inspect various influential factors including sample size, sequencing depth, effect size, and basal expression ranges. We integrate two modules in magpie: (i) a flexible and realistic simulator module to synthesize m6A sequencing data based on real data; and (ii) a power assessment module to examine a set of comprehensive evaluation metrics.</div

Comparing power evaluation results between major DMR detection methods TRESS (A)-(B) and exomePeak2 (C)-(D).

Targeted power and FDC are shown at various Odds Ratios (OR, representing effect size) and sample sizes. A nominal FDR value of 0.05 is used to define significance. Points on the line plots are averaged over N = 100 simulations.</p

Statistical power evaluation metrics for DMR detection, at various sample sizes and FDR thresholds.

(A) Power versus sample size, with each line presenting one FDR cutoff. (B)-(D) Similar to (A) but for other metrics: targeted power, FDC, and FDR. Targeted power and FDC are computed for DMRs with |β|≥2. Each point on the line plots is an averaged value over N = 100 simulations based on real MeRIP-seq data.</p

The confusion matrix in m⁶A DMR detection, when taking biological significance into consideration.

The confusion matrix in m6A DMR detection, when taking biological significance into consideration.</p