Mcgarryriggs1148

Strand cross-correlation profiles are used for both peak calling pre-analysis and quality control (QC) in chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis. Despite its potential for robust and accurate assessments of signal-to-noise ratio (S/N) because of its peak calling independence, it remains unclear what aspects of quality such strand cross-correlation profiles actually measure.

We introduced a simple model to simulate the mapped read-density of ChIP-seq and then derived the theoretical maximum and minimum of cross-correlation coefficients between strands. The results suggest that the maximum coefficient of typical ChIP-seq samples is directly proportional to the number of total mapped reads and the square of the ratio of signal reads, and inversely proportional to the number of peaks and the length of read-enriched regions. Simulation analysis supported our results and evaluation using 790 ChIP-seq data obtained from the public database demonstrated high consistency between cependent QC and will help in the evaluation of peak call analysis in ChIP-seq experiments.

We present the first theoretical insights into the strand cross-correlation, and the results reveal the potential and the limitations of strand cross-correlation analysis. Our quality assessment framework using VSN provides peak call-independent QC and will help in the evaluation of peak call analysis in ChIP-seq experiments.Glucose uptake and adenosine triphosphate (ATP) generation are important for the survival and growth of endothelial cells. An increase of glucose uptake under hypoxia was previously shown to be associated with the increased expression of glucose transporters (GLUTs). However, the regulation of GLUT trafficking to the cell surface has not been examined in detail. Here, we report the characterization of GLUT1 translocation to the plasma membrane during hypoxia in endothelial cells. Human umbilical vein endothelial cells (HUVECs) were exposed to hypoxia (1% O2) for 12 h, which significantly induced GLUT1 expression and translocation to the plasma membrane. GLUT1 translocation was associated with a decrease of intracellular ATP by hypoxia. Decreasing ATP levels with antimycin-A and 2-deoxyglucose induced GLUT1 translocation under normoxia. The induction of hypoxia-inducible factor-1α under normoxia did not influence the cell surface expression of GLUT1 or cellular ATP concentration. Interestingly, the translocation of GLUT1 induced by hypoxia was inhibited by the ATP-sensitive potassium (KATP) channel inhibitor glibenclamide, while the mitochondrial KATP channel inhibitor 5-HD did not influence GLUT1 translocation during hypoxia. These observations indicate that a decrease of intracellular ATP triggers GLUT1 translocation to the plasma membrane and is mediated by KATP channels, which would contribute to glucose uptake in HUVECs during hypoxia.

Nutrigenomics is an emerging science that studies the relationship between genes, diet and nutrients that can help prevent chronic disease. The development of this science depends on whether the public accept its application; therefore, predicting their intention to adopt it is important for its successful implementation.

This study aims to analyse Malaysian stakeholders' intentions to adopt nutrigenomics, and determines the factors that influence their intentions.

A survey was conducted based on the responses of 421 adults (aged 18 years and older) and comprising two stakeholder groups healthcare providers (n = 221) and patients (n = 200) who were located in the Klang Valley, Malaysia. The SPSS software was used to analyse the descriptive statistics of intention to adopt nutrigenomics and the SmartPLS software was used to determine the predicting factors affecting their decisions to adopt nutrigenomics.

The results show that the stakeholders perceived the benefits of nutrigenomics as outweighing its e a complex decision-making process where all the previously mentioned factors interact. Although the results showed that the stakeholders in Malaysia were highly positive towards nutrigenomics, they were also cautious about adopting it.

Crustaceans express several classes of receptor genes in their antennules, which house olfactory sensory neurons (OSNs) and non-olfactory chemosensory neurons. Transcriptomics studies reveal that candidate chemoreceptor proteins include variant Ionotropic Receptors (IRs) including both co-receptor IRs and tuning IRs, Transient Receptor Potential (TRP) channels, Gustatory Receptors, epithelial sodium channels, and class A G-protein coupled receptors (GPCRs). The Caribbean spiny lobster, Panulirus argus, expresses in its antennules nearly 600 IRs, 17 TRP channels, 1 Gustatory Receptor, 7 epithelial sodium channels, 81 GPCRs, 6 G proteins, and dozens of enzymes in signaling pathways. However, the specific combinatorial expression patterns of these proteins in single sensory neurons are not known for any crustacean, limiting our understanding of how their chemosensory systems encode chemical quality.

The goal of this study was to use transcriptomics to describe expression patterns of chemoreceptor genes in OStion and others likely involved in modulation. Our results also suggest differences in receptor expression in OSNs vs. other chemosensory neurons.

Our results provide an initial view of the combinatorial expression patterns of receptor molecules in single OSNs in one species of decapod crustacean, including receptors directly involved in olfactory transduction and others likely involved in modulation. Our results also suggest differences in receptor expression in OSNs vs. other chemosensory neurons.

One hypothesis for the function of sleep is that it serves as a mechanism to conserve energy. Recent studies have suggested that increased sleep can be an adaptive mechanism to improve survival under food deprivation in Drosophila melanogaster. To test the generality of this hypothesis, we compared sleep and its plastic response to starvation in a temperate and tropical population of Drosophila melanogaster.

We found that flies from the temperate population were more starvation resistant, and hypothesized that they would engage in behaviors that are considered to conserve energy, including increased sleep and reduced movement. Surprisingly, temperate flies slept less and moved more when they were awake compared to tropical flies, both under fed and starved conditions, therefore sleep did not correlate with population-level differences in starvation resistance. In contrast, total sleep and percent change in sleep when starved were strongly positively correlated with starvation resistance within the tropicaby natural selection.

Disease resilience is the ability to maintain performance under pathogen exposure but is difficult to select for because breeding populations are raised under high health. Selection for resilience requires a trait that is heritable, easy to measure on healthy animals, and genetically correlated with resilience. selleck kinase inhibitor Natural antibodies (NAb) are important parts of the innate immune system and are found to be heritable and associated with disease susceptibility in dairy cattle and poultry. Our objective was to investigate NAb and total IgG in blood of healthy, young pigs as potential indicator traits for disease resilience.

Data were from Yorkshire x Landrace pigs, with IgG and IgM NAb (four antigens) and total IgG measured by ELISA in blood plasma collected ~ 1 week after weaning, prior to their exposure to a natural polymicrobial challenge. Heritability estimates were lower for IgG NAb (0.12 to 0.24, + 0.05) and for total IgG (0.19+0.05) than for IgM NAb (0.33 to 0.53, + 0.07) but maternal effects were larger gions, with several candidate genes for immune response.

Levels of NAb in blood of healthy young piglets are heritable and potential genetic indicators of resilience to polymicrobial disease.

Levels of NAb in blood of healthy young piglets are heritable and potential genetic indicators of resilience to polymicrobial disease.

In silico promoter prediction represents an important challenge in bioinformatics as it provides a first-line approach to identifying regulatory elements to support wet-lab experiments. Historically, available promoter prediction software have focused on sigma factor-associated promoters in the model organism E. coli. As a consequence, traditional promoter predictors yield suboptimal predictions when applied to other prokaryotic genera, such as Pseudomonas, a Gram-negative bacterium of crucial medical and biotechnological importance.

We developed SAPPHIRE, a promoter predictor for σ70 promoters in Pseudomonas. This promoter prediction relies on an artificial neural network that evaluates sequences on their similarity to the - 35 and - 10 boxes of σ70 promoters found experimentally in P. aeruginosa and P. putida. SAPPHIRE currently outperforms established predictive software when classifying Pseudomonas σ70 promoters and was built to allow further expansion in the future.

SAPPHIRE is the first predictive tool for bacterial σ70 promoters in Pseudomonas. link2 SAPPHIRE is free, publicly available and can be accessed online at www.biosapphire.com . Alternatively, users can download the tool as a Python 3 script for local application from this site.

SAPPHIRE is the first predictive tool for bacterial σ70 promoters in Pseudomonas. SAPPHIRE is free, publicly available and can be accessed online at www.biosapphire.com . Alternatively, users can download the tool as a Python 3 script for local application from this site.

Gene selection refers to find a small subset of discriminant genes from the gene expression profiles. How to select genes that affect specific phenotypic traits effectively is an important research work in the field of biology. The neural network has better fitting ability when dealing with nonlinear data, and it can capture features automatically and flexibly. link3 In this work, we propose an embedded gene selection method using neural network. The important genes can be obtained by calculating the weight coefficient after the training is completed. In order to solve the problem of black box of neural network and further make the training results interpretable in neural network, we use the idea of knockoffs to construct the knockoff feature genes of the original feature genes. This method not only make each feature gene to compete with each other, but also make each feature gene compete with its knockoff feature gene. This approach can help to select the key genes that affect the decision-making of neural networks.

We use maize carotenoids, tocopherol methyltransferase, raffinose family oligosaccharides and human breast cancer dataset to do verification and analysis.

The experiment results demonstrate that the knockoffs optimizing neural network method has better detection effect than the other existing algorithms, and specially for processing the nonlinear gene expression and phenotype data.

The experiment results demonstrate that the knockoffs optimizing neural network method has better detection effect than the other existing algorithms, and specially for processing the nonlinear gene expression and phenotype data.