Self-Powered Wireless Temperature and Vibration Monitoring System by Weak Vibrational Energy for Industrial Internet of Things

Developing self-powered smart wireless sensor networks by harvesting industrial environmental weak vibration energy remains a challenge and an impending need for enabling the widespread rollout of the industrial internet of things (IIoT). This work reports a self-powered wireless temperature and vibration monitoring system (WTVMS) based on a vibrational triboelectric nanogenerator (V-TENG) and a piezoelectric nanogenerator (PENG) for weak vibration energy collection and information sensing. Therein, the V-TENG can scavenge weak vibration energy down to 80 μm to power the system through a power management module, while the PENG is able to supply the frequency signal to the system by a comparison circuit. In an industrial vibration environment where the vibration frequency and amplitude are 20 Hz and 100 μm, respectively, the WTVMS can upload temperature and frequency information on the equipment to the cloud in combination with the narrowband IoT technology to realize real-time information monitoring. Furthermore, the WTVMS can work continuously for more than 2 months, during which the V-TENG can operate up to 100 million cycles, achieving ultrahigh stability and durability. By integrating weak vibration energy harvesting and active sensing technology, the WTVMS can be used for real-time online monitoring and early fault diagnosis of vibration equipment, which has great application prospects in industrial production, machinery manufacturing, traffic transportation, and intelligent IIoT

Supplementary table: Eliminating drug target interference withspecific antibody or its F(ab?)2 fragment inthe bridging immunogenicity assay

Background: DB-1003 is a humanized anti-IgE monoclonal antibody with higher affinity than omalizumab. In the affinity capture elution (ACE)-based bridging electrochemiluminescent immunoassay (ECLIA) for antibodies to DB-1003, monkey serum IgE caused false-positive results. Materials & methods: The target specific antibody or its F(ab?)2 fragment was used to mitigate drug target interference in an ACE-based bridging ECLIA for the detection of anti-DB-1003 antibodies. Results: The sensitivity of the developed assay was at least 100 ng/ml. When the ADA concentration was 250 ng/ml, the assay tolerated at least 20.0 μg/ml of the monkey IgE. Conclusion: Incorporating the target-specific antibody or its F(ab?)2 mfragment can overcome the interference from monkey serum IgE in ACE-based bridging ECLIA for anti- DB-1003 antibody detection.</p

Combinatorial Pharmacophore Modeling of Organic Cation Transporter 2 (OCT2) Inhibitors: Insights into Multiple Inhibitory Mechanisms

Organic cation transporter 2 (OCT2) is responsible for the entry step of many drugs in renal elimination, of which the changing activity may cause unwanted drug–drug interactions (DDIs). To develop drugs with favorable safety profile and provide instruction for rational clinical drug administration, it is of great interest to investigate the multiple mechanisms of OCT2 inhibition. In this study, we designed a combinatorial scheme to screen the optimum combination of pharmacophores from a pool of hypotheses established based on 162 OCT2 inhibitors. Among them, one single pharmacophore hypothesis represents a potential binding mode that may account for one unique inhibitory mechanism, and the obtained pharmacophore combination describes the multimechanisms of OCT2 inhibition. The final model consists of four individual pharmacophores, i.e., DHPR18, APR2, PRR5 and HHR4. Given a query ligand, it is considered as an inhibitor if it matches at least one of the hypotheses, or a noninhibitor if it fails to match any of four hypotheses. Our combinatorial pharmacophore model performs reasonably well to discriminate inhibitors and noninhibitors, yielding an overall accuracy around 0.70 for a test set containing 81 OCT2 inhibitors and 218 noninhibitors. Intriguingly, we found that the number of matched hypotheses was positively correlated with inhibition rate, which coincides with the pharmacophore modeling result of P-gp substrate binding. Further analysis suggested that the hypothesis PRR5 was responsible for competitive inhibition of OCT2, and other hypotheses were important for interaction between the inhibitor and OCT2. In light of the results, a hypothetical model for inhibiting transporting mediated by OCT2 was proposed

Extra Sugar on Vancomycin: New Analogues for Combating Multidrug-Resistant <i>Staphylococcus aureus</i> and Vancomycin-Resistant <i>Enterococci</i>

Lipophilic substitution on vancomycin is an effective strategy for the development of novel vancomycin analogues against drug-resistant bacteria by enhancing bacterial cell wall interactions. However, hydrophobic structures usually lead to long elimination half-life and accumulative toxicity; therefore, hydrophilic fragments were also introduced to the lipo-vancomycin to regulate their pharmacokinetic/pharmacodynamic properties. Here, we synthesized a series of new vancomycin analogues carrying various sugar moieties on the seventh-amino acid phenyl ring and lipophilic substitutions on vancosamine with extensive structure–activity relationship analysis. The optimal analogues indicated 128–1024-fold higher activity against methicillin-susceptible <i>S. aureus</i>, vancomycin-intermediate resistant <i>S. aureus</i> (VISA), and vancomycin-resistant <i>Enterococci</i> (VRE) compared with that of vancomycin. In vivo pharmacokinetics studies demonstrated the effective regulation of extra sugar motifs, which shortened the half-life and addressed concerns of accumulative toxicity of lipo-vancomycin. This work presents an effective strategy for lipo-vancomycin derivative design by introducing extra sugars, which leads to better antibiotic-like properties of enhanced efficacy, optimal pharmacokinetics, and lower toxicity

BC005512 is a member of the GLN family of murine endogenous retrovirus.

<p>Sequence alignment between BC005512, BC062922 and MMERGLN_I. Locations of the genechip probe, quantitative PCR primers (BC-F and BC-R) and BC siRNAs are shown.</p

Down-regulating BC expression suppressed cell growth in several mouse cell lines.

<p>(<b>A, C</b> and <b>E</b>) Quantitative PCR results showing knock-down efficiency of BC siRNAs in NIH/3T3, Hepa 1–6 or SV40 MES 13 cells at 48 h after siRNA transfection. Data were mean ± s.d. of at least three independent experiments. (<b>B, D</b> and <b>F</b>) Cell numbers of NIH/3T3, Hepa 1–6 and SV40 MES 13 cells at indicated times after siRNA transfection. Data were mean ± s.d. of at least three independent experiments performed in triplicate. Values shown on top of bars are <i>P</i> values <i>vs</i> nonsense.</p

Expression of BC was specifically induced by GTXs in NIH/3T3 cells.

<p>Data from quantitative PCR showing transcriptional expression of BC in NIH/3T3 cells treated with genotoxic or non-genotoxic chemicals for indicated time. Data were mean ± s.d. of three independent experiments.</p

Weight score for genotoxic stress responsive gene selection in the <i>in vivo</i> microarray study (liver, B6C3F1).

1<p>“V” represents values.</p>2<p>“S” represents score. Only the top 20 genes are shown. A full list is attached in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035010#pone.0035010.s012" target="_blank">Table S3</a>.</p><p>Detailed scoring rules are described in the supporting information. “Pink cells” represent up-regulation and “blue cells” represent down-regulation.</p><p><b>Specificity</b> = (number of total pink cells in GTXs)/(number of total pink cells in GTXs and NGTXs); <b>Ave ratio</b> = average of ratios of all pink cells in GTXs; <b>Positive condition</b> = number of total pink cells in GTXs. Since DEN was duplicated, each pink cell was considered 0.5; <b>Positive chemical</b> = number of GTXs with at least one pink cell; <b><i>P</i></b><b> value</b> was calculated by <i>t</i> test of signal intensity between GTXs and NGTXs in GeneSpring software; <b>Basal</b> represents basal expression level, equals to log₁₀ value of signal intensity of control animals; <b>Reverse change</b> reflects opposite change of gene expression in different treatment groups. Reverse change = number of blue cells in NGTXs - number of blue cells in GTXs; <b>CV%</b> = 100×SD/MEAN% based on the signal intensity of all control animals. <b>Total score</b> = Score of 2× Specificity + Ave ratio + Positive condition + Positive chemical + <i>P</i> value + 0.5× Basal + 0.5× Reverse change + 0.5× CV%.</p

Selection of sensitive and specific genotoxic stress responsive genes.

<p>(<b>A</b>) Hierarchical clustering of top 50 scored up-regulated genes shown in gene symbol. Red and green indicate up-regulation and down-regulation, respectively. The orange box represents genes whose expression could distinguish GTXs from NGTXs. The blue box represents the gene with the highest score, BC005512. (<b>B</b> and <b>C</b>) Microarray and quantitative PCR (qPCR) data showing BC expression levels in livers of mice dosed with indicated chemicals at 4 h or 20 h after administration. Microarray data represented pooled samples from 4 animals per group. Quantitative PCR data were mean ± s.d. (n = 4).</p

Model compounds selected in the <i>in vivo</i> microarray study.

1<p>Abbr: Abbreviation;</p>2<p>CAS: Chemical Abstracts Service.</p