Notes

A powerful, Counter-Selection-Based Way for Prophage Curing inside Pseudomonas aeruginosa Strains.
002 or less for all recycling bins), the preference was mainly affected by slot largeness for combustible and incombustible waste bins. According to these results, this study concludes that the hypothesis was supported in particular for color preference. On the other hand, on-site experiments showed that the effect of only "impressive" color was too weak to improve waste separation. To encourage waste sorting using well-designed recycling bins, combination of preferred design items is necessary. Intensive usage of well-designed recycling bins for frequent perception opportunities is recommended as an environmental feedback to support sufficient design preference.High solid anaerobic digestion (HSAD) is a promising technology for the treatment of organic waste. Mixing process in HSAD is quite difficult with long mixing time, poor homogenization, significant short-circuiting and stagnant zones. However, the mass transfer in mixing process in HSAD has not been visualized due to the lack of a proper method. In this study, a novel approach for experimentally quantifying the mass transfer in HSAD's mixing process was proposed in a mixing tank equipped with multistage impellers by means of the Laser Induced Fluorescence (LIF) technique. Flow field was investigated for better illustrating the mass transfer, thus Particle Image Velocimetry (PIV) and Computational Fluid Dynamics (CFD) technique were conducted for flow field measurement. The obtained results revealed that the feedstock tended to accumulate around the impeller in the HSAD system, especially near the 1st stage. The tracer diffused rapidly around the 1st impeller in t = 10 s, but the diffusion around the 2nd impeller was quite tough even after 4 h 08 min 23 s. And prolonging mixing time could not significantly improve the flow pattern along with the mixing. In this study, new insight was thrown into the visualization of the mass transfer in mixing process within a HSAD reactor. The visualization of mass transfer in the mixing process in HSAD could offer reference information to the study of the mixing process of HSAD even in full-scale.The recycling of agricultural waste is a global challenge to the sustainable development of agriculture. By using corn stalks, we studied the feasibility of combining anaerobic fermentation and pyrolysis processes to produce both fermentated liquid feed and biologically modified biocharas cadmium adsorbents. Anaerobic ensiling enhanced the biodegradation of corn stalks by increasing crude protein and reducing fiber contents. compound library chemical After 24-h anaerobic fermentation, corn stalks silage was decomposed into the liquid filtrate and non-fermented residue. Fermented liquid feed (FLF) was prepared by storing feed and liquid filtrate (14.0, wt/wt) in a closed tank at 20 °C for 4 days, which showed desired properties (pH less then 4.5, lactic acid bacteria greater than 9.0 lg cfu g-1, lactic acid greater than 100 mmol L-1). The non-fermented residue was pyrolyzed at 500 °C to prepare biologically modified biochar (BCB24). In comparison with pristine biochar produced from corn stalks (CB), anaerobic ensiling and anaerobic fermentation significantly increased the surface area, oxygen-containing functional groups, as well as mineral components in BCB24. The maximum sorption capacity of Cd(II) for BCB24 was 2.1 times of CB, suggesting that BCB24 is an effective adsorbent for Cd(II) removal from water. Our results indicated that coupling anaerobic fermentation and pyrolysis technology can significantly improve the efficiency of corn stalks recycling.The ability to maintain balance when turning is essential to functional and independent living. Due to the lack of neuromuscular ankle control on the prosthetic side in people with a transtibial amputation (TTA), turning is likely more challenging. The purpose of this study was to quantify how people with TTA maintain dynamic balance during 90-degree turns made with the prosthesis on the inside and outside of the turn compared to people without amputation. Eight participants with TTA and eight age-, height-, and sex- matched non-amputee controls performed left and right 90-degree step turns at a self-selected speed. The primary outcomes were range of whole-body angular momentum and positive and negative contributions of six segment groups (head/trunk, pelvis, arms, and legs) to whole-body angular momentum during the continuation stride. Participants with TTA had greater range of frontal- and sagittal-plane whole-body angular momentum when turning with the prosthesis on the inside compared controls. They also had a greater range of whole-body angular momentum in all planes of motion when turning with the prosthesis on the inside compared to outside of the turn. The contributions for the head/trunk and inside and outside legs differed between groups and turns, suggesting altered interactions between segment momenta to compensate for the reduced contribution of the amputated leg. This study provides insight into possible training paradigms to reduce the high incidence of turn related falls in people with TTA and, potentially, ways to alter prosthetic function to promote balance control.Testing a prediction is fundamental to scientific experiments. Where biomechanical experiments involve analysis of 1-Dimensional (waveform) data, sample size estimation should consider both 1D variance and hypothesised 1D effects. This study exemplifies 1D sample size estimation using typical biomechanical signals and contrasts this with 0D (discrete) power analysis. For context, biomechanics papers from 2018 and 2019 were reviewed to characterise current practice. Sample size estimation occurred in approximately 4% of 653 papers and reporting practice was mixed. To estimate sample sizes, common biomechanical signals were sourced from the literature and 1D effects were generated artificially using the open-source power1d software. Smooth Gaussian noise was added to the modelled 1D effect to numerically estimate the sample size required. Sample sizes estimated using 1D power procedures varied according to the characteristics of the dataset, requiring only small-to-moderate sample sizes of approximately 5-40 to achieve target powers of 0.
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