Linkage between Culturable Mineral-Weathering Bacteria and Their Weathering Effectiveness Along a Soil Profile

<div><p>In this study, we used culture-dependent methodology to characterize the weathering effectiveness and community of culturable mineral-weathering bacteria in an ultisol profile. A total of 261 isolates were obtained and found to have the ability to weather biotite. The proportions of the highly effective Si and Al solubilizers were significantly higher in the D and E horizons than in the A, B, C, and F horizons, while the A, B, C, and F horizons had the similar proportion of the highly effective Si solubilizers. The B and F horizons had the lowest proportion of the highly effective Al solubilizers. The D horizon had the maximum proportion of the highly effective Fe solubilizers. Lowest proportion of the highly effective Fe solubilizers was observed in the A and F horizons. The 261 mineral-weathering isolates were affiliated with 39 bacterial species within 19 genera. <i>Burkholderia anthina</i> from the A and B horizons, <i>Burkholderia stabilis</i> from the C, D, and E horizons, and <i>Curtobacterium citreum</i> from the F horizon had the significantly higher ability to release Si, Al, and Fe from biotite. The results showed the diverse mineral-weathering bacteria and the linkage between the weathering species and their weathering effectiveness along a soil profile.</p></div

Kaplan-Meier curves for the <i>BRM</i>-741 indel under the co-dominant genetic model in the colorectal cancer cases.

<p>P value of the log-rank test is 0.017.</p

Distribution of baseline characteristics of the study cohorts.

<p>Distribution of baseline characteristics of the study cohorts.</p

<i>BRM</i> promoter indels and colorectal cancer risk.

<p><i>BRM</i> promoter indels and colorectal cancer risk.</p

Hybrid Principal Component Analysis Denoising Enables Rapid, Label-Free Morpho-Chemical Quantification of Individual Nanoliposomes

Laser tweezers Raman spectroscopy enables multiplexed, quantitative chemical and morphological analysis of individual bionanoparticles such as drug-loaded nanoliposomes, yet it requires minutes-scale acquisition times per particle, leading to a lack of statistical power in typical small-sized data sets. The long acquisition times present a bottleneck not only in measurement time but also in the analytical throughput, as particle concentration (and thus throughput) must be kept low enough to avoid swarm measurement. The only effective way to improve this situation is to reduce the exposure time, which comes at the expense of increased noise. Here, we present a hybrid principal component analysis (PCA) denoising method, where a small number (∼30 spectra) of high signal-to-noise ratio (SNR) training data construct an effective principal component subspace into which low SNR test data are projected. Simulations and experiments prove the method outperforms traditional denoising methods such as the wavelet transform or traditional PCA. On experimental liposome samples, denoising accelerated data acquisition from 90 to 3 s, with an overall 4.5-fold improvement in particle throughput. The denoised data retained the ability to accurately determine complex morphochemical parameters such as lamellarity of individual nanoliposomes, as confirmed by comparison with cryo-EM imaging. We therefore show that hybrid PCA denoising is an efficient and effective tool for denoising spectral data sets with limited chemical variability and that the RR-NTA technique offers an ideal path for studying the multidimensional heterogeneity of nanoliposomes and other micro/nanoscale bioparticles