Time Differential Pricing Model of Urban Rail Transit Considering Passenger Exchange Coefficient

Passenger exchange coefficient is a significant factor which has great impact on the pricing model of urban rail transit. This paper introduces passenger exchange coefficient into a bi-level programming model with time differential pricing for urban rail transit by analysing variation regularity of passenger flow characteristics. Meanwhile, exchange cost coefficient is also considered as a restrictive factor in the pricing model. The improved particle swarm optimisation algorithm (IPSO) was applied to solve the model, and simulation results show that the proposed improved pricing model can effectively realise stratification of fares for different time periods with different routes. Taking Line 2 and Line 8 of the Beijing rail transit network as an example, the simulation result shows that passenger flows of Line 2 and Line 8 in peak hours decreased by 9.94% and 19.48% and therefore increased by 32.23% and 44.96% in off-peak hours, respectively. The case study reveals that dispersing passenger flows by means of fare adjustment can effectively drop peak load and increase off-peak load. The time differential pricing model of urban rail transit proposed in this paper has great influences on dispersing passenger flow and ensures safety operation of urban rail transit. It is also a valuable reference for other metropolitan rail transit operating companies

Proteomic Analysis of Silk Viability in Maize Inbred Lines and Their Corresponding Hybrids

<div><p>A long period of silk viability is critical for a good seed setting rate in maize (<i>Zea mays</i> L.), especially for inbred lines and hybrids with a long interval between anthesis and silking. To explore the molecular mechanism of silk viability and its heterosis, three inbred lines with different silk viability characteristics (Xun928, Lx9801, and Zong3) and their two hybrids (Xun928×Zong3 and Lx9801×Zong3) were analyzed at different developmental stages by a proteomic method. The differentially accumulated proteins were identified by mass spectrometry and classified into metabolism, protein biosynthesis and folding, signal transduction and hormone homeostasis, stress and defense responses, and cellular processes. Proteins involved in nutrient (methionine) and energy (ATP) supply, which support the pollen tube growth in the silk, were important for silk viability and its heterosis. The additive and dominant effects at a single locus, as well as complex epistatic interactions at two or more loci in metabolic pathways, were the primary contributors for mid-parent heterosis of silk viability. Additionally, the proteins involved in the metabolism of anthocyanins, which indirectly negatively regulate local hormone accumulation, were also important for the mid-parent heterosis of silk viability. These results also might imply the developmental dependence of heterosis, because many of the differentially accumulated proteins made distinct contributions to the heterosis of silk viability at specific developmental stages.</p></div

Protein–protein interaction networks obtained <i>in silico</i> using String database, with COG functions.

<p>Protein–protein interaction networks obtained <i>in silico</i> using String database, with COG functions.</p

The Key Transcription Factor Expression in the Developing Vestibular and Auditory Sensory Organs: A Comprehensive Comparison of Spatial and Temporal Patterns

Inner ear formation requires that a series of cell fate decisions and morphogenetic events occur in a precise temporal and spatial pattern. Previous studies have shown that transcription factors, including Pax2, Sox2, and Prox1, play important roles during the inner ear development. However, the temporospatial expression patterns among these transcription factors are poorly understood. In the current study, we present a comprehensive description of the temporal and spatial expression profiles of Pax2, Sox2, and Prox1 during auditory and vestibular sensory organ development in mice. Using immunohistochemical analyses, we show that Sox2 and Pax2 are both expressed in the prosensory cells (the developing hair cells), but Sox2 is later restricted to only the supporting cells of the organ of Corti. In the vestibular sensory organ, however, the Pax2 expression is localized in hair cells at postnatal day 7, while Sox2 is still expressed in both the hair cells and supporting cells at that time. Prox1 was transiently expressed in the presumptive hair cells and developing supporting cells, and lower Prox1 expression was observed in the vestibular sensory organ compared to the organ of Corti. The different expression patterns of these transcription factors in the developing auditory and vestibular sensory organs suggest that they play different roles in the development of the sensory epithelia and might help to shape the respective sensory structures

Functional categories of differentially accumulated proteins in the inbred lines and its corresponding hybrid combinations.

<p>Functional categories of differentially accumulated proteins in the inbred lines and its corresponding hybrid combinations.</p

Differentially accumulated proteins identified by MS during silk development at four sampling stages in three inbred lines.

<p>Notes</p><p>^a Spot No. corresponds to labels in 2-DE map.</p><p>^b Maximum fold changes between different developmental stages were calculated by ANOVA.</p><p>^c GenBank accession number of protein spot.</p><p>^d Protein name in NCBI database.</p><p>^e Protein scores were derived from ions scores as a non-probabilistic basis for ranking protein hits.</p><p>^f Confidence interval of the identified proteins.</p><p>^g Gene name retrieved from maize sequence (<a href="http://ensembl.gramene.org/Zea_mays/Info/Index" target="_blank">http://ensembl.gramene.org/Zea_mays/Info/Index</a>) by cDNA blast.</p><p>^h Physical position determined by blast function in MaizeGDB.</p><p>Differentially accumulated proteins identified by MS during silk development at four sampling stages in three inbred lines.</p

Differentially accumulated proteins identified by MS between hybrids and corresponding inbred lines during different sampling stages.

<p>Notes</p><p>^a Spot No. corresponds to labels in 2-DE map.</p><p>^b Maximum fold changes among three groups (female line, male line, and hybrid combination) were calculated by ANOVA.Y/N means that difference between them was present or absent.</p><p>^c Heterotic patterns were examined following Hoecker et al. (2008). “A” indicates protein spots that had no significant difference in average spot intensities with midparent value at 0.05 level. Average spot intensities of proteins that deviated significantly from the mid-parent value of the parental lines at the 0.05 cut-off level were defined as non-additive proteins. Based on this premise, “+” and “-” represented protein spot intensities identified in F₁ hybrids that were similar to the high parent and low parent values, respectively. “+ +” and “- -” represented the protein spot intensities identified in F₁ hybrids that were significantly different from the high parent and the low parent values, respectively. “+ -” represented the protein spot intensities identified in F₁ hybrids that fell in between the mid-parent and the high parent or the mid-parent and the low parent values.</p><p>^d Hybrids Xun928×Zong3 and Lx9801×Zong3 are abbreviated as XZ and LZ, respectively. D₈, D₁₀, and D₁₂ represent sampling stages.</p><p>^e GenBank accession number of protein spot.</p><p>^f Protein name in NCBI database.</p><p>^g Proteins scores were derived from ions scores as a non-probabilistic basis for ranking protein hits.</p><p>^h Confidence interval of the identified protein.</p><p>ⁱ Gene name retrieved from maize sequence (<a href="http://ensembl.gramene.org/Zea_mays/Info/Index" target="_blank">http://ensembl.gramene.org/Zea_mays/Info/Index</a>) by cDNA blast.</p><p>^j Physical position determined by blast function in MaizeGDB.</p><p>Differentially accumulated proteins identified by MS between hybrids and corresponding inbred lines during different sampling stages.</p