Factor loadings and correlation between factor scores.

<p>Factor loadings and correlation between factor scores.</p

A histogram of the frequencies of different mean eating consistency rates of different individuals, aggregated across all episodes.

<p>A histogram of the frequencies of different mean eating consistency rates of different individuals, aggregated across all episodes.</p

Mean eating consistency rate at each hour from 7AM until midnight, aggregated across all days.

<p>Grey area denotes 95% confidence intervals.</p

Composite index of consistency.

<p>A meal was considered consistent, if it was similar to the regular meal (black circle). If the meal was smaller, larger, healthier, or unhealthier than the regular meal, it was considered inconsistent (grey circle). Percentages denote the rates of various behaviours. The sum of percentages is more than 100% as several inconsistent behaviours could occur at the same time.</p

Summary of models tested.

<p>Summary of models tested.</p

Detailed plots of the interactions in the final model.

<p>X axes denotes factor scores of personality traits or age in years, y axis denotes mean eating consistency rate for different situations. Mean value is depicted by bold red/blue line, gray areas denote 95% confidence intervals.</p

A streamgraph of the frequency of different types of eating situations at prompts 1–6, summed across all days.

<p>Alcohol = alcohol was consumed; away = away from home; no = eating-only event; PE = physical exercise; PW = physical work; social = eating with others.</p

Mean eating consistency rate at prompts 1–6, aggregated across all days.

<p>Grey area denotes 95% confidence intervals. Panel below shows the mean time each prompt took place, along with standard deviations (SD). For instance, the mean time when people responded to 1^st prompt was 9.4 hours (9:24 AM), with a standard deviation of 2.1 hours (2 hours, 6 minutes).</p

Transformation of the B Ring to the C Ring of Bryostatins by Csp³–H Amination and <i>Z</i> to <i>E</i> Isomerization

An interesting approach to transform the B ring of bryostatins to the C ring has been developed. The key tactics of the approach feature an intramolecular Csp³–H bond amination to form spirocyclic hemiaminal, which undergoes ring opening to afford the C ring found in bryostatin 17. The subsequent epoxidation/oxidation sequence results in <i>Z</i> to <i>E</i> isomerization of the <i>exo</i>-cyclic enoate, delivering the common precursor, which could be transformed into the C ring found in bryostatins 1, 2, 4–9, 12, 14, and 15

Copper Oxide Nanoparticles Induce Lysogenic Bacteriophage and Metal-Resistance Genes in <i>Pseudomonas aeruginosa</i> PAO1

The intensive use of metal-based nanoparticles results in their continuous release into the environment, leading to potential risks for human health and microbial ecosystems. Although previous studies have indicated that nanoparticles may be toxic to microorganisms, there is a scarcity of data available to assess the underlying molecular mechanisms of inhibitory and biocidal effects of nanoparticles on microorganisms. This study used physiological experiments, microscopy, live/dead staining, and the genome-wide RNA sequencing to investigate the multiple responses of <i>Pseudomonas aeruginosa</i> to the exposure of copper oxide nanoparticles (CuO NPs). The results for the first time show that CuO NPs induce lysogenic bacteriophage, which might render defective within a bacterial host. The presence of CuO NPs causes nitrite accumulation and great increases in N₂O emissions. Respiration is likely inhibited as denitrification activity is depleted in terms of decreased transcript levels of most denitrification genes. Meanwhile, CuO NPs exposure significantly up-regulated gene expression for those coding for copper resistance, resistance-nodulation-division, P-type ATPase efflux, and cation diffusion facilitator transporters. Our findings offer insights into the interaction between environmental bacteria and CuO NPs at the transcriptional level and, thus, improve our understanding of potential risks of nanoparticles on microbial ecosystems and public health