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Supplementary Materials. (PDF 1263 kb

SI_JRocInterface.pdf from Stochastic resonances in a distributed genetic broadcasting system: the NF<i>κ</i>B/I<i>κ</i>B paradigm

Gene regulatory networks must relay information from extracellular signals to downstream genes in an efficient, timely and coherent manner. Many complex functional tasks such as the immune response require system-wide broadcasting of information not to one but to many genes carrying out distinct functions whose dynamical binding and unbinding characteristics are widely distributed. In such broadcasting networks, the intended target sites are also often dwarfed in number by the even more numerous non-functional binding sites. Taking the genetic regulatory network of NF<i>κ</i>B as an exemplary system we explore the impact of having numerous distributed sites on the stochastic dynamics of oscillatory broadcasting genetic networks pointing out how resonances in binding cycles control the network's specificity and performance. We also show that active kinetic regulation of binding and unbinding through molecular stripping of DNA bound transcription factors can lead to a higher coherence of gene-co-expression and synchronous clearance

Supplementary Materials from Molecular stripping, targets and decoys as modulators of oscillations in the <i>NF–κB/IκBα/DNA</i> genetic network

Eukaryotic transcription factors in the <i>NF–κB</i> family are central components of an extensive genetic network which activates cellular responses to inflammation and to a host of other external stressors. This network consists of feedback loops that involve the inhibitor <i>IκBα</i>, numerous downstream functional targets and still more numerous binding sites that do not appear to be directly functional. Under steady stimulation the regulatory network of <i>NF–κB</i> becomes oscillatory and temporal patterns of <i>NF–κB</i> pulses appear to govern the patterns of downstream gene expression needed for immune response. Understanding how the information from external stress passes to oscillatory signals and is then ultimately relayed to gene expression is a general issue in systems biology. Recently <i>in vitro</i> kinetic experiments as well as molecular simulations suggest that active stripping of <i>NF–κB</i> by <i>IκBα</i> from its binding sites can modify the traditional systems biology view of <i>NF</i>–<i>κB</i>/<i>IκBα</i> gene circuits. In this work we revise the commonly adopted minimal model of the <i>NF–κB</i> regulatory network to account for the presence of the large number of binding sites for <i>NF–κB</i> along with dissociation from these sites which may proceed either by passive unbinding or by active molecular stripping. We identify regimes where the kinetics of target and decoy unbinding and molecular stripping enter a dynamic tug of war which may either compensate each other or amplify nuclear <i>NF–κB</i> activity, leading to distinct oscillatory patterns. Our finding that decoys and stripping play a key role in shaping the <i>NF–κB</i> oscillations suggests strategies to control <i>NF–κB</i> responses by introducing artificial decoys therapeutically

Main Chain Dendronized Polymers: Design, Synthesis, and Application in the Second-Order Nonlinear Optical (NLO) Area

For the first time, two nonlinear optical (NLO) main chain dendronized polymers, <b>MDPG1</b> and <b>MDPG2</b>, were prepared through the Suzuki coupling reaction by utilizing low-generation dendrimers as the monomers. These polymers inherit the advantages of both main chain polymers (which usually demonstrate good stability of the NLO effect) and dendronized polymers (which usually possess a large NLO coefficient) simultaneously. For <b>MDPG2</b>, its NLO coefficients <i>d</i>₃₃ value and <i>d</i>_33(∞) value are up to 116 and 20 pm/V, respectively, very similar to those of the normal dendronized polymer <b>DPG2</b>. Interestingly, thanks to its main chain structure, the onset temperature for the decay of its <i>d</i>₃₃ value was tested to be 100 °C, 30 °C higher than that of <b>DPG2</b>

Epistatic Effects on Abdominal Fat Content in Chickens: Results from a Genome-Wide SNP-SNP Interaction Analysis

<div><p>We performed a pairwise epistatic interaction test using the chicken 60 K single nucleotide polymorphism (SNP) chip for the 11^th generation of the Northeast Agricultural University broiler lines divergently selected for abdominal fat content. A linear mixed model was used to test two dimensions of SNP interactions affecting abdominal fat weight. With a threshold of P<1.2×10⁻¹¹ by a Bonferroni 5% correction, 52 pairs of SNPs were detected, comprising 45 pairs showing an Additive×Additive and seven pairs showing an Additive×Dominance epistatic effect. The contribution rates of significant epistatic interactive SNPs ranged from 0.62% to 1.54%, with 47 pairs contributing more than 1%. The SNP-SNP network affecting abdominal fat weight constructed using the significant SNP pairs was analyzed, estimated and annotated. On the basis of the network’s features, SNPs Gga_rs14303341 and Gga_rs14988623 at the center of the subnet should be important nodes, and an interaction between GGAZ and GGA8 was suggested. Twenty-two quantitative trait loci, 97 genes (including nine non-coding genes), and 50 pathways were annotated on the epistatic interactive SNP-SNP network. The results of the present study provide insights into the genetic architecture underlying broiler chicken abdominal fat weight.</p></div

Epistatic network among SNPs affecting abdominal fat weight (AFW) in NEAUHLF.

<p>A node represents a SNP. The chromosome in which the SNP is located is shown in the circle. A pair of SNPs connected by an edge has a significant effect. The colors of the nodes represent P-values of the interaction (P<1×10⁻¹³ = red; P<1×10⁻¹² = blue; P<1×10⁻¹¹ = green; P<1×10⁻¹⁰ = white). The color of the edge indicates the epistatic effect type (AA = red; AD = purple).</p

Hydrogen Bond Network for Efficient and Stable Fully Ambient Air-Processed Perovskite Solar Cells with Over 21% Efficiency

Chemical passivation serves as a highly effective strategy for mitigating defects to obtain efficient and stable perovskite solar cells. However, concerns about the stability and environmental-friendliness of molecular modulators have emerged with regard to the passivation effect. In this article, we introduce a judicious hydrogen bond network (HBN) composed of imidazole (ImA) and salicylic acid (SA) to address this issue. The ImA-SA HBN features multiple functional groups (amino and carboxyl) that simultaneously passivate various defects, including I– vacancies and uncoordinated Pb2+ ions located at grain boundaries and surfaces of the perovskite films. Remarkably, the ImA-SA HBN effectively bridges perovskite grains and interfaces between the perovskite films and hole transport materials, which enhances the transport of photogenerated carriers. Moreover, cyclic ImA-SA, characterized by steric hindrance, inhibits ion migration derived from grain boundaries of the perovskite films. Consequently, the efficiency of the champion device processed in fully ambient air exceeded 21% and did not decline obviously after 3380 h of storage in the air environment (20 ± 5 °C, 30 ± 5% RH, and without encapsulation). This work offers a promising approach to diminishing the defects in perovskite grain boundaries and surfaces, enhancing optoelectronic properties and facilitating the creation of efficient and stable perovskite solar cells processed in fully ambient air

Description of SNPs epistatic interaction network.

1<p>The greatest degree = The greatest number of nodes in the subnet;</p>2<p>Structure = Topological structure of the subnet;</p>3<p>Chromosomes = Chromosomes contained in the subnet.</p

Regions and genes of the subnets shown in Figure 1.

1<p>Start = The start of the region;</p>2<p>End = The end of the region.</p

Genome-wide significant pairwise epistatic interactive SNP pairs for abdominal fat weight, P<1.20×10⁻¹¹.

1<p>GGA F = The first chromosome in the pairwise epistasis analysis; GGA S = The second chromosome in the pairwise epistasis analysis; AA = Additive×Additive effect, AD = Additive×Dominance effect, DA = Dominance×Additive effect, DD = Dominance×Dominance effect; P-value = P-value of the effect being tested; c = The contribution rate (%) of every significant epistatic interactive SNP pair.</p