A Collaborative Optimization Model for Ground Taxi Based on Aircraft Priority

Large hub airports have gradually become the “bottleneck” of the air transport network. To alleviate the “bottleneck” effect, optimizing the taxi scheduling is one of the solutions. This paper establishes a scheduling optimization model by introducing priority of aircraft under collaborative decision-making mechanism, and a genetic algorithm is designed to verify the scheduling model by simulating. Optimization results show that the reliability of the model and the adjusted genetic algorithm have a high efficiency. The taxiing time decreases by 2.26% when compared with an empirical method and the flights with higher priorities are assigned better taxi routes. It has great significance in reducing flight delays and cost of operation

Simultaneous calibration: a joint optimization approach for multiple Kinect and external cameras

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Aberrant Expression Profiles of lncRNAs and Their Associated Nearby Coding Genes in the Hippocampus of the SAMP8 Mouse Model with AD

The senescence-accelerated mouse prone 8 (SAMP8) mouse model is a useful model for investigating the fundamental mechanisms involved in the age-related learning and memory deficits of Alzheimer's disease (AD), while the SAM/resistant 1 (SAMR1) mouse model shows normal features. Recent evidence has shown that long non-coding RNAs (lncRNAs) may play an important role in AD pathogenesis. However, a comprehensive and systematic understanding of the function of AD-related lncRNAs and their associated nearby coding genes in AD is still lacking. In this study, we collected the hippocampus, the main area of AD pathological processes, of SAMP8 and SAMR1 animals and performed microarray analysis to identify aberrantly expressed lncRNAs and their associated nearby coding genes, which may contribute to AD pathogenesis. We identified 3,112 differentially expressed lncRNAs and 3,191 differentially expressed mRNAs in SAMP8 mice compared to SAMR1 mice. More than 70% of the deregulated lncRNAs were intergenic and exon sense-overlapping lncRNAs. Gene Ontology (GO) and pathway analyses of the AD-related transcripts were also performed and are described in detail, which imply that metabolic process reprograming was likely related to AD. Furthermore, six lncRNAs and six mRNAs were selected for further validation of the microarray results using quantitative PCR, and the results were consistent with the findings from the microarray. Moreover, we analyzed 780 lincRNAs (also called long "intergenic" non-coding RNAs) and their associated nearby coding genes. Among these lincRNAs, AK158400 had the most genes nearby (n = 13), all of which belonged to the histone cluster 1 family, suggesting regulation of the nucleosome structure of the chromosomal fiber by affecting nearby genes during AD progression. In addition, we also identified 97 aberrant antisense lncRNAs and their associated coding genes. It is likely that these dysregulated lncRNAs and their associated nearby coding genes play a role in the development and/or progression of AD

The distribution of reference gene expression levels in <i>longissimus dorsi</i> muscle (A) and <i>biceps femoris</i> muscle (B).

<p>Means and medians are indicated by asterisks and lines, respectively. The boxes encompass the 25th to the 75th percentiles. Whisker caps denote the maximum and minimum values.</p

Gene expression stabilities and rankings of reference genes as calculated by geNorm.

<p>Rank-order of gene expression stability is shown for <i>longissimus dorsi</i> muscle (A-1), <i>biceps femoris</i> muscle (B-1) and the combined group (C-1) according to the average expression stability values (<i>M</i>) for the reference genes from the least stable (left) to the most stable (right). Pairwise variation analysis (<i>V</i>) to determine the optimal number of reference genes for data normalization in <i>longissimus dorsi</i> muscle (A-2), <i>biceps femoris</i> muscle (B-2) and the combined group (C-2).</p

Selection of Reference Genes for Gene Expression Studies Related to Intramuscular Fat Deposition in <i>Capra hircus</i> Skeletal Muscle

<div><p>The identification of suitable reference genes is critical for obtaining reliable results from gene expression studies using quantitative real-time PCR (qPCR) because the expression of reference genes may vary considerably under different experimental conditions. In most cases, however, commonly used reference genes are employed in data normalization without proper validation, which may lead to incorrect data interpretation. Here, we aim to select a set of optimal reference genes for the accurate normalization of gene expression associated with intramuscular fat (IMF) deposition during development. In the present study, eight reference genes (<i>PPIB</i>, <i>HMBS</i>, <i>RPLP0</i>, <i>B2M</i>, <i>YWHAZ</i>, <i>18S</i>, <i>GAPDH</i> and <i>ACTB</i>) were evaluated by three different algorithms (geNorm, NormFinder and BestKeeper) in two types of muscle tissues (<i>longissimus dorsi</i> muscle and <i>biceps femoris</i> muscle) across different developmental stages. All three algorithms gave similar results. <i>PPIB</i> and <i>HMBS</i> were identified as the most stable reference genes, while the commonly used reference genes <i>18S</i> and <i>GAPDH</i> were the most variably expressed, with expression varying dramatically across different developmental stages. Furthermore, to reveal the crucial role of appropriate reference genes in obtaining a reliable result, analysis of <i>PPARG</i> expression was performed by normalization to the most and the least stable reference genes. The relative expression levels of <i>PPARG</i> normalized to the most stable reference genes greatly differed from those normalized to the least stable one. Therefore, evaluation of reference genes must be performed for a given experimental condition before the reference genes are used. <i>PPIB</i> and <i>HMBS</i> are the optimal reference genes for analysis of gene expression associated with IMF deposition in skeletal muscle during development.</p></div

Expression profiling of <i>GAPDH</i> and <i>18S</i> in skeletal muscles at different developmental stages.

<p>Gene expression data for both <i>longissimus dorsi</i>(A、C) and <i>biceps femoris</i> muscle (B、D)were normalized to the geometric mean of <i>PPIB&HMBS</i> and are relative to expression at 12 months following the 2^−ΔΔCT method [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121280#pone.0121280.ref034" target="_blank">34</a>]. Error bars depict SD. Superscript capital letters indicate significant differences (<i>P<0</i>.<i>05</i>).</p