Au/Lum/RhB@Ag-DMcT ICP-Based Double-Ratio Colorimetric and Fluorometric Dual Mode Assay and Multi-Responsive Coffee Ring Chips for Point-of-Use Analysis of Phosphate Ions.

In this work, by fully exploring the stimulus response of the guest-functionalized infinite coordination polymers (ICPs), a double-ratio colorimetric and fluorometric dual mode assay and multi-responsive coffee ring chips for point-of-use analysis of phosphate ions (Pi) were proposed. First, the complex host-guest interactions were rationally designed to obtain Au/Lum/RhB@Ag-DMcT ICPs. The composite ICPs exhibited a purple-blue color resulted from the modulated localized surface plasmon resonance (LSPR) of the Au core, and a blue fluorescence color stemmed from the unique aggregation-induced-emission (AIE) of Luminol (Lum) and the aggregation-caused-quenching (ACQ) of rhodamine B (RhB). With the presence of Pi, the host-guest interactions of the shell within Au/Lum/RhB@Ag-DMcT ICPs were interrupted to release Au core, Lum, and RhB in a dispersed state. Consequently, the color of the solution changed to purple-red (the mixed color of the Au core and RhB guest), and the fluorescence color turned to orange-red (AIE of Lum decreased, while the ACQ of RhB recovered). This constituted the sensing mechanism for dual-mode Pi assay with the double ratiometric response. Second, during the stimulus response, the surface wettability/size/amount of Au/Lum/RhB@Ag-DMcT ICPs simultaneously altered. These changes were reflected in the form of the coffee ring deposition pattern variances on the glass substrate and served as signal readouts for the exploration of multi-responsive coffee ring chips for the first time. Quantitative Pi detection with high accuracy and reliability in real samples was thereby realized, which offered an opportunity for the point-of-use analysis of Pi in resources-limited areas in a high-throughput fashion.

DNA methylation loci identification for pan-cancer early-stage diagnosis and prognosis using a new distributed parallel partial least squares method.

Aberrant methylation is one of the early detectable events in many tumors, which is very promising for pan-cancer early-stage diagnosis and prognosis. To efficiently analyze the big pan-cancer methylation data and to overcome the co-methylation phenomenon, a MapReduce-based distributed and parallel-designed partial least squares approach was proposed. The large-scale high-dimensional methylation data were first decomposed into distributed blocks according to their genome locations. A distributed and parallel data processing strategy was proposed based on the framework of MapReduce, and then latent variables were further extracted for each distributed block. A set of pan-cancer signatures through a differential co-expression network followed by statistical tests was further identified based on their gene expression profiles. In total, 15 TCGA and 3 GEO datasets were used as the training and testing data, respectively, to verify our method. As a result, 22,000 potential methylation loci were selected as highly related loci with early-stage pan-cancer diagnosis. Of these, 67 methylation loci were further identified as pan-cancer signatures considering their gene expression as well. The survival analysis as well as pathway enrichment analysis on them shows that not only these loci may serve as potential drug targets, but also the proposed method may serve as a uniform framework for signature identification with big data.

Overexpression of FAP7, MIG3, TMA19, or YLR392c confers resistance to arsenite on Saccharomyces cerevisiae.

To elucidate mechanisms of arsenic toxicity and biological defense mechanisms against arsenic, we searched for genes that, when overexpressed, conferred arsenite resistance on yeast. Employing a Saccharomyces cerevisiae open reading frame (ORF) library, four genes associated with arsenite resistance, FAP7, MIG3, TMA19, and YLR392c, were identified.