Automated Intelligent Monitoring and the Controlling Software System for Solar Panels

The inspection of the solar panels on a periodic basis is important to improve longevity and ensure performance of the solar system. To get the most solar potential of the photovoltaic (PV) system is possible through an intelligent monitoring & controlling system. The monitoring & controlling system has rapidly increased its popularity because of its user-friendly graphical interface for data acquisition, monitoring, controlling and measurements. In order to monitor the performance of the system especially for renewable energy source application such as solar photovoltaic (PV), data-acquisition systems had been used to collect all the data regarding the installed system. In this paper the development of a smart automated monitoring & controlling system for the solar panel is described, the core idea is based on IoT (the Internet of Things). The measurements of data are made using sensors, block management data acquisition modules, and a software system. Then, all the real-time data collection of the electrical output parameters of the PV plant such as voltage, current and generated electricity is displayed and stored in the block management. The proposed system is smart enough to make suggestions if the panel is not working properly, to display errors, to remind about maintenance of the system through email or SMS, and to rotate panels according to a sun position using the Ephemeral table that stored in the system. The advantages of the system are the performance of the solar panel system which can be monitored and analyzed

Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53

Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets. Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region. Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes

Use of tobacco and alcohol by Swiss primary care physicians: a cross-sectional survey

BACKGROUND: Health behaviours among doctors has been suggested to be an important marker of how harmful lifestyle behaviours are perceived. In several countries, decrease in smoking among physicians was spectacular, indicating that the hazard was well known. Historical data have shown that because of their higher socio-economical status physicians take up smoking earlier. When the dangers of smoking become better known, physicians began to give up smoking at a higher rate than the general population. For alcohol consumption, the situation is quite different: prevalence is still very high among physicians and the dangers are not so well perceived. To study the situation in Switzerland, data of a national survey were analysed to determine the prevalence of smoking and alcohol drinking among primary care physicians. METHODS: 2'756 randomly selected practitioners were surveyed to assess subjective mental and physical health and their determinants, including smoking and drinking behaviours. Physicians were categorised as never smokers, current smokers and former smokers, as well as non drinkers, drinkers (AUDIT-C < 4 for women and < 5 for men) and at risk drinkers (higher scores). RESULTS: 1'784 physicians (65%) responded (men 84%, mean age 51 years). Twelve percent were current smokers and 22% former smokers. Sixty six percent were drinkers and 30% at risk drinkers. Only 4% were never smokers and non drinkers. Forty eight percent of current smokers were also at risk drinkers and 16% of at risk drinkers were also current smokers. Smoking and at risk drinking were more frequent among men, middle aged physicians and physicians living alone. When compared to a random sample of the Swiss population, primary care physicians were two to three times less likely to be active smokers (12% vs. 30%), but were more likely to be drinkers (96% vs. 78%), and twice more likely to be at risk drinkers (30% vs. 15%). CONCLUSION: The prevalence of current smokers among Swiss primary care physicians was much lower than in the general population in Switzerland, reflecting that the hazards of smoking are well known to doctors. However, the opposite was found for alcohol use, underlining the importance of making efforts in this area to increase awareness among physicians of the dangers of alcohol consumption

Novel Evolved Immunoglobulin (Ig)-Binding Molecules Enhance the Detection of IgM against Hepatitis C Virus

Detection of specific antibodies against hepatitis C virus (HCV) is the most widely available test for viral diagnosis and monitoring of HCV infections. However, narrowing the serologic window of anti-HCV detection by enhancing anti-HCV IgM detection has remained to be a problem. Herein, we used LD5, a novel evolved immunoglobulin-binding molecule (NEIBM) with a high affinity for IgM, to develop a new anti-HCV enzyme-linked immunosorbent assay (ELISA) using horseradish peroxidase-labeled LD5 (HRP-LD5) as the conjugated enzyme complex. The HRP-LD5 assay showed detection efficacy that is comparable with two kinds of domestic diagnostic kits and the Abbott 3.0 kit when tested against the national reference panel. Moreover, the HRP-LD5 assay showed a higher detection rate (55.9%, 95% confidence intervals (95% CI) 0.489, 0.629) than that of a domestic diagnostic ELISA kit (Chang Zheng) (53.3%, 95% CI 0.463, 0.603) in 195 hemodialysis patient serum samples. Five serum samples that were positive using the HRP-LD5 assay and negative with the conventional anti-HCV diagnostic ELISA kits were all positive for HCV RNA, and 4 of them had detectable antibodies when tested with the established anti-HCV IgM assay. An IgM confirmation study revealed the IgM reaction nature of these five serum samples. These results demonstrate that HRP-LD5 improved anti-HCV detection by enhancing the detection of anti-HCV IgM, which may have potential value for the early diagnosis and screening of hepatitis C and other infectious diseases

Highly multiplexed subcellular RNA sequencing in situ

Understanding the spatial organization of gene expression with single-nucleotide resolution requires localizing the sequences of expressed RNA transcripts within a cell in situ. Here, we describe fluorescent in situ RNA sequencing (FISSEQ), in which stably cross-linked complementary DNA (cDNA) amplicons are sequenced within a biological sample. Using 30-base reads from 8102 genes in situ, we examined RNA expression and localization in human primary fibroblasts with a simulated wound-healing assay. FISSEQ is compatible with tissue sections and whole-mount embryos and reduces the limitations of optical resolution and noisy signals on single-molecule detection. Our platform enables massively parallel detection of genetic elements, including gene transcripts and molecular barcodes, and can be used to investigate cellular phenotype, gene regulation, and environment in situ

Embedding mRNA Stability in Correlation Analysis of Time-Series Gene Expression Data

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R2 that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R2 for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R2 value among genes coding for a transient complex

Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

Peer reviewedPublisher PD