A quantitative analysis of monochromaticity in genetic interaction networks

<p>Abstract</p> <p>Background</p> <p>A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed.</p> <p>Results</p> <p>In this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes.</p> <p>Conclusion</p> <p>In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).</p

Emotion and Concentration Integrated System: Applied to the Detection and Analysis of Consumer Preference

With the expansion of consumer market, the appearance becomes an important issue when consumers make decisions under the situation of similar qualities and contents. Accordingly, to attract consumers, companies cost and take much attention on product appearance. Compared to using questionnaires individually, obtaining humans’ thoughts directly from their brains can accurately grasp the actual preference of consumers, which can provide effective and precious decisions for companies. \ In this study, consumers’ brainwaves which are related to concentration and emotion are extracted by wearing a portable and wireless Electroencephalography (EEG) device. The extracted EEG data are then trained by using perceptron learning algorithm (PLA) to make the judgments of concentration and emotion work well with each subject. They are then applied to the detection and analysis of consumer preference. Finally, the questionnaires are also performed and used as the reference on training process. They are integrated with brainwaves data to create one prediction model which can improve the accuracy significantly. The Partial Least Squares is used to compare the correlation between different factors in the model, to ensure the test can accurately meet consumers’ thoughts

Neutrophils as one of the major haptoglobin sources in mastitis affected milk

The antioxidant haptoglobin (Hp) is an acute-phase protein responsive to infectious and inflammatory diseases. Hp and somatic cell counts (SCC) are sharply elevated in bovine milk following intramammary administration of endotoxin or bacteria. However, the sources of milk Hp responsible for such increases are not fully understood. The purpose of this study was to define the source of milk Hp from dairy cows with naturally occurring mastitis. Quarter milk samples selected from 50 dairy cows were separated into four groups according to SCC as group A: < 100 (n = 19); B: 100–200 (n = 10); C: 201–500 (n = 10); and D: > 500 × 103 (n = 11) cells/mL. Our results reveal that milk Hp concentrations were correlated with SCC (r = 0.742; P < 0.01), and concentrations in group D were ~10-fold higher than in group A. Reverse transcriptase-polymerase chain reaction (RT-PCR) analysis indicates that the milk somatic cells from group D were not only capable of synthesizing Hp but could also markedly increase Hp mRNA expression. Western blot, immunocytochemistry, double confocal immunofluorescence, and Hp releasing experiments demonstrate that neutrophils were associated with the biosynthesis and release of Hp in milk. It further shows that Hp was significantly elevated in the epithelium of mammary gland tissue with mastitis and was also expressed in the cultured mammary epithelial cells. We propose that neutrophils and epithelial cells may play an essential role in elevating milk Hp in addition to previous suggestions that Hp may be derived from mammary tissues and circulation