Microfluidic Droplet-Facilitated Hierarchical Assembly for Dual Cargo Loading and Synergistic Delivery.

Bottom-up hierarchical assembly has emerged as an elaborate and energy-efficient strategy for the fabrication of smart materials. Herein, we present a hierarchical assembly process, whereby linear amphiphilic block copolymers are self-assembled into micelles, which in turn are accommodated at the interface of microfluidic droplets via cucurbit[8]uril-mediated host-guest chemistry to form supramolecular microcapsules. The monodisperse microcapsules can be used for simultaneous carriage of both organic (Nile Red) and aqueous-soluble (fluorescein isothiocyanate-dextran) cargo. Furthermore, the well-defined compartmentalized structure benefits from the dynamic nature of the supramolecular interaction and offers synergistic delivery of cargos with triggered release or through photocontrolled porosity. This demonstration of premeditated hierarchical assembly, where interactions from the molecular to microscale are designed, illustrates the power of this route toward accessing the next generation of functional materials and encapsulation strategies

Logistic Regression models regressing 7-day abstinence onto social support, social influence, demographics, and cigarettes per day smoked for family, friend, and online friend networks (n = 123).

Logistic Regression models regressing 7-day abstinence onto social support, social influence, demographics, and cigarettes per day smoked for family, friend, and online friend networks (n = 123).</p

Negative Binomial Regression models of social support and social influence constructs regressed onto network characteristics and demographics, cigarettes per day smoked for family, friend and online friend networks (n = 123).

Negative Binomial Regression models of social support and social influence constructs regressed onto network characteristics and demographics, cigarettes per day smoked for family, friend and online friend networks (n = 123).</p

Descriptive statistics: Demographics, cigarettes per day smoked, social network characteristics, social support, social influence and 7 day abstinence for the family, friend, and online friend networks (n = 123).

Descriptive statistics: Demographics, cigarettes per day smoked, social network characteristics, social support, social influence and 7 day abstinence for the family, friend, and online friend networks (n = 123).</p

S1 Data -

Adults’ social network ties serve multiple functions and play prominently in quitting smoking. We examined three types of adults’ egocentric social networks, including family, friends, and friends online to investigate how two network characteristics with major relevance to health behavior, network size and tie closeness, related to the emotional and confidant support and to pro- and anti-smoking social influence these ties may transmit. We also examine whether the social support and social influence constructs related to smoking abstinence. We utilized baseline and 7-day abstinence survey data from 123 adult current smokers attempting to quit prior to the start of a randomized controlled quit-smoking trial of a social support intervention for quitting smoking on Twitter. To examine study relationships, we estimated Negative Binomial Regression models and Logistic Regression models. For all networks, network size and tie closeness related positively to most of the social support and social influence constructs, with tie closeness related most strongly, especially for online friends. Family pro-smoking social influence related negatively to smoking abstinence, and there were marginally negative relationships for family emotional support and family confidant support. Online friend emotional support had a marginally positive relationship with smoking abstinence. Overall, our findings indicated the importance of the social support and social influence functions of each type of network tie, with larger networks and closer ties related to higher levels of social support and social influence. Moreover, family network pro-smoking social influence may compromise abstinence while emotional support from online friend network ties may reinforce it.</div

Functional Classification of SVGs in Three Microorganisms

<div><p><i>M</i> is the total number of genes in a COG broad functional category, and <i>m</i> is the number of SVGs within that category. <i>r</i> ( = <i>m/M</i>) is the proportion of SVGs in that category. The <i>p</i>-value is calculated using a hypergeometric distribution: let <i>N</i> = number of genes in the genome; <i>n</i> = number of SVGs identified; <i>M</i> = number of genes belonging to a particular category; <i>m</i> = number of SVGs belonging to a particular category:</p> <p> </p><p></p><p></p> <p>The set of lineage-specific genes has been excluded in each genome to avoid the possible skew it brings to the estimation of significance. The significance level is set at 0.01. Cells with <i>p</i>-value less than 0.01 are shaded.</p></div

2D Representation of the Distance Matrix Computed from the Variable and Conserved Domains in a Group of Similar HKs

<p>The upper triangle shows the variable domains, the lower one the conserved domains. Amino acid sequence distances are calculated by the PROTDIST program using the Dayhoff PAM matrix. The sequence from each species is the best match (<i>E</i>-value < 1<i>E</i>-10) in that genome to the query E. coli gene. Abbreviations for organisms: Ec, Escherichia coli K12; Ps, Pseudomonas syringae pv. syringae B728a; Rm, Ralstonia metallidurans; Rs, Ralstonia solanacearum; Li, Listeria innocua; Tm, Thermotoga maritime; Ml, Mycobacterium leprae; Mt, Mycobacterium tuberculosis CDC1551; No, <i>Nostoc</i> sp. PCC 7120; Ef, Enterococcus faecalis; Bs, Bacillus subtilis; Ne, Nitrosomonas europaea; Sy, <i>Synechococcus</i> sp. PCC 7942; At, Agrobacterium tumefaciens. The PROTDIST program is included in the PHYLIP software package version 3.5 (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020081#pbio-0020081-Felsenstein1" target="_blank">Felsenstein 1989</a>).</p

Paralogous Genes in SVGs

<div><p>(A) Paralog families in SVGs for four microorganisms. The x-axis shows the number of paralogs for each SVG. The y-axis shows the number of SVGs. The inset figure shows the percentage of genes with different numbers of paralogs for SVGs and fully conserved genes in E. coli genome. The x-axis is the number of paralogs, and the y-axis is the percentage.</p> <p>(B) Contingency tables to examine the dependence between SVG and paralogous gene. χ² statistics are computed using standard formula.</p></div