Protein Sensing and Cell Discrimination Using a Sensor Array Based on Nanomaterial-Assisted Chemiluminescence

Cross-reactive sensor arrays, known as “chemical noses”, offer an alternative to time-consuming analytical methods. Here, we report a sensor array based on nanomaterial-assisted chemiluminescence (CL) for protein sensing and cell discrimination. We have found that the CL efficiencies are improved to varied degrees for a given protein or cell line on catalytic nanomaterials. Distinct CL response patterns as “fingerprints” can be obtained on the array and then identified through linear discriminant analysis (LDA). The sensing of 12 kinds of proteins at three concentrations, as well as 12 types of human cell lines among normal, cancerous, and metastatic, has been performed. Compared with most fluorescent or colorimetric approaches which rely on the strong interaction between analytes and sensing elements, our array offers the advantage of both sensitivity and reversibility

Dual-Channel Sensing of Volatile Organic Compounds with Semiconducting Nanoparticles

Extracting multidimensional information from an individual transducer simultaneously is a promising alternative sensing strategy to traditional sensors. Here, we proposed a novel dual channel sensing method with simultaneously recording conductivity change of sensing material and chemiluminescence emission during catalytic oxidation of volatile organic compounds on tin oxide nanoparticles. The orthogonal and complementary electrical and optical signals have been obtained for each compound, which have been applied to discriminate 20 volatile organic compounds using hierarchical cluster analysis (HCA). Unknown samples from three groups at concentrations of 0.2%, 0.6%, and 1.0% have been successfully classified using linear discriminant analysis (LDA) with accuracies of 98.3%, 96.7%, and 98.3%, respectively. This dual channel sensing mode is a complement of semiconducting type gas sensors and quite promising for the development of chemical sensor arrays with multimode transducing principles

Additional file 1 of Concomitant elevated serum levels of tenascin, MMP-9 and YKL-40, suggest ongoing remodeling of the heart up to 3 months after cardiac surgery after normalization of the revascularization markers

Additional file 1: Differences at preoperative CCL-28 levels in three pre-existing conditions

Parameters in CNN modelling.

<p>Parameters in CNN modelling.</p

Detection of Amyloid β Oligomers by a Fluorescence Ratio Strategy Based on Optically Trapped Highly Doped Upconversion Nanoparticles-SiO₂@Metal–Organic Framework Microspheres

Alzheimer’s disease (AD), known as a progressive neurodegenerative disorder, has had a terrible impact on the health of aged people. Due to its severity, early diagnosis of AD is significant to retard the progress and provide timely treatment. Here, we report a fluorescence ratio detection of AD biomarker amyloid β oligomers (AβOs) by combining highly doped upconversion nanoparticles-SiO2@metal–organic framework/black hole quencher (H-USM/BHQ-1) microspheres with optical tweezer (OT) microscopic imaging. Optical trapping a single microsphere not only avoids the interference of fluid viscosity but also provides a high power density laser source to efficiently stimulate upconversion luminescence (UCL) of highly doped upconversion nanoparticles (H-UCNPs). Under this condition, H-UCNPs show stronger UCL and greater power-dependent properties compared to low-doped ones. Moreover, the closely packed quenching molecules BHQ-1 on a metal–organic framework (ZIF-8) exhibit excellent quenching efficiency for upconversion 525 and 540 nm emission. Also, the luminescent resonance energy transfer efficiency reaches 89.58%. When different concentrations of AβOs are present, the UCL540 recovers due to the decomposition of ZIF-8 and the release of BHQ-1. Using 540 and 654 nm emission ratio of highly doped UCNPs as reporters, the limit of detection reaches 28.4 pM for the quantitative determination of AβOs. Besides, this strategy is able to selectively quantify the AβO concentration. Therefore, we demonstrated the combination of optical trapping and highly doped UCNPs which is applied for the detection of AβOs with high sensitivity and specificity

Historical loads in the 20 zones.

<p>The loads in the majority of the zones are approximately measured at the 100,000 kW level; however, some zones have loads near the 10,000 kW level, such as Zone 1, Zone 13 and Zone 14. Zone 4 has the lowest load of less than 1,000 kW. Large differences in the load fluctuations of these 20 zones are observed.</p

Variables and their binary representations.

<p>Variables and their binary representations.</p

miR9863a and miR9863b.1/b.2 specifically regulate group I <i>Mla</i> alleles.

<p>(<b>A</b>) <i>Mla</i> alleles are classified into three groups according to the SNP haplotype in the miR9863 binding site. The two SNPs differ among <i>Mla</i> groups are highlighted. (<b>B</b>) <i>Mla</i> alleles of group I, but not group II and III, are regulated by miR9863a and miR9863b.1/b.2. <i>Mla</i> genes of group I (<i>Mla28</i>, <i>Mla32</i>), group II (<i>Mla2</i>, <i>Mla6</i>) and group III (<i>Mla10</i>, <i>Mla12</i>) were respectively co-expressed with either <i>MIR9863a</i> (upper panels) or <i>MIR9863b</i> (lower panels) in <i>N. benthamiana</i> as described in <b><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004755#pgen-1004755-g002" target="_blank">Fig. 2</a></b>. Protein levels of MLA or actin were determined by immunoblotting with an anti-HA or anti-actin antibody; Rubisco was included as a loading control. The asterisks indicate non-specific signals.</p

Actual and backcasted/forecasted loads for the 20 zones and the total load.

<p>The sub-figures (labelled Zone-1 to Zone-21) represent Zone 1 to Zone 20 and the total load measured in kW. The actual power load and the modelled load (including the backcasted load for the eight missing weeks and the forecasted load for one week) are shown, where blue indicates the actual load and green indicates the modelled load. The same colours and symbol codes are applied to each zone and to the total load (denoted by Zone 21).</p

Inputs and outputs for forecasting the load at 1:00 from Jan. 1 to Jan. 7, 2004, for Zone 1 (in binary format).

<p>The top of the figure denotes the category of the input/output of the model: Xa denotes the calendar information (year, month, holiday, and weekday), Xb denotes the temperature, Xc denotes the historical temperature, Xd denotes the historical load, and Y denotes the output.</p