Identification and analysis of differentially expressed long non-coding RNAs between multiparous and uniparous goat (<i>Capra hircus</i>) ovaries

<div><p>Long non-coding RNAs (lncRNAs) play important roles in almost all biological processes. However, there is little information on the effects of lncRNAs on ovulation and lambing rates. In the present study, we used high-throughput RNA sequencing to identify differentially expressed lncRNAs between the ovaries of multiparous (Mul) and uniparous (Uni) Anhui White goats. Among the 107,255,422 clean reads, 183,754 lncRNAs were significantly differentially expressed between the Uni and Mul. Among them, 455 lncRNAs were co-expressed between the two samples, whereas, 157,523 lncRNAs were uniquely expressed in the Uni, and 25,776 uniquely lncRNAs were expressed in the Mul. Through Cis role analysis, 24 lncRNAs were predicted to overlap with cis-regulatory elements, which involved in Progesterone-mediated oocyte maturation, Steroid biosynthesis, Oocyte meiosis, and gonadotropin-releasing hormone (GnRH) signaling pathway. These 4 pathways were related to ovulation, and the KEGG pathway analysis on target genes of the differentially expressed lncRNAs confirmed this results. In addition, 10 lncRNAs harbored precursors of 40 miRNAs, such as TCONS_00320849 related to a mature miRNA sequence, miR-365a, which was reported to be related to proliferation, were annotated in the precursor analysis of miRNAs. The present expand the understanding of lncRNA biology and contribute to the annotation of the goat genome. The study will provide a resource for lncRNA studies of ovulation and lambing.</p></div

Up/down stream lncRNA of a gene.

<p>Up/down stream lncRNA of a gene.</p

Expression statistic of different lncRNAs and mRNA.

<p>Expression statistic of different lncRNAs and mRNA.</p

Families prediction of lncRNAs.

<p>Families prediction of lncRNAs.</p

Real-time PCR results of randomly selected differentially expressed lncRNAs.

<p>Note: X-axis represents selected 8 differential expressed transcripts in two libraries. Here, GAPDH was chosen as the reference gene. Relative expression value per selected transcripts between uniparous goats (Uni) and multiparous goats (Mul) samples was calculated (y-axis). Superscript letters indicate significant difference at the level of 0.05.</p

Transcript coverage stat of uniparous goats (Uni) and multiparou goats (Mul).

<p>Transcript coverage stat of uniparous goats (Uni) and multiparou goats (Mul).</p

Stat chart of up-down-regulated expressed genes.

<p>Stat chart of up-down-regulated expressed genes.</p

Apple Fruit Diameter and Length Estimation by Using the Thermal and Sunshine Hours Approach and Its Application to the Digital Orchard Management Information System

<div><p>In apple cultivation, simulation models may be used to monitor fruit size during the growth and development process to predict production levels and to optimize fruit quality. Here, Fuji apples cultivated in spindle-type systems were used as the model crop. Apple size was measured during the growing period at an interval of about 20 days after full bloom, with three weather stations being used to collect orchard temperature and solar radiation data at different sites. Furthermore, a 2-year dataset (2011 and 2012) of apple fruit size measurements were integrated according to the weather station deployment sites, in addition to the top two most important environment factors, thermal and sunshine hours, into the model. The apple fruit diameter and length were simulated using physiological development time (PDT), an indicator that combines important environment factors, such as temperature and photoperiod, as the driving variable. Compared to the model of calendar-based development time (CDT), an indicator counting the days that elapse after full bloom, we confirmed that the PDT model improved the estimation accuracy to within 0.2 cm for fruit diameter and 0.1 cm for fruit length in independent years using a similar data collection method in 2013. The PDT model was implemented to realize a web-based management information system for a digital orchard, and the digital system had been applied in Shandong Province, China since 2013. This system may be used to compute the dynamic curve of apple fruit size based on data obtained from a nearby weather station. This system may provide an important decision support for farmers using the website and short message service to optimize crop production and, hence, economic benefit.</p></div

Error analysis of fruit diameter and length simulation in the spring and autumn of 2011, 2012, and 2013.

<p>^t and ^u: Regression coefficient determined by simple linear regression <i>Y = a+bX</i>, where estimated values = <i>X</i>, measured values = <i>Y</i>;</p><p>^v: coefficient of determination;</p><p>^w: Willmott agreement index;</p><p>^x: Mean absolute error;</p><p>^y: Mean bias error;</p><p>^Z: Root mean square error.</p><p>Error analysis of fruit diameter and length simulation in the spring and autumn of 2011, 2012, and 2013.</p

Comparison between estimated and measured fruit size values in 2011 and 2012 using the PDT model.

<p>Comparison between estimated and measured fruit size values in 2011 and 2012 using the PDT model.</p