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Ice recrystallization inhibition (IRI) proteins are thought to play an important role in conferring freezing tolerance in plants. Two genes encoding IRI proteins, LpIRI-a and LpIRI-b, were isolated from a relatively cold-tolerant perennial ryegrass cv. Caddyshack. Amino acid alignments among the IRI proteins revealed the presence of conserved repetitive IRI-domain motifs (NxVxxG/NxVxG) in both proteins. Quantitative reverse transcriptase PCR (qRT-PCR) analysis indicated that LpIRI-a was up-regulated approximately 40-fold while LpIRI-b was up-regulated sevenfold after just 1 h of cold acclimation, and by 7 days of cold acclimation the transcripts had increased 8,000-fold for LpIRI-a and 1,000-fold for LpIRI-b. Overexpression of either LpIRI-a or LpIRI-b gene in Arabidopsis increased survival rates of the seedlings following a freezing test under both cold-acclimated and nonacclimated conditions. For example, without cold acclimation a -4 degrees C treatment reduced the wild type's survival rate to an average of 73%, but resulted in survival rates of 85-100% for four transgenic lines. With cold acclimation, a -12 degrees C treatment reduced the wild type's survival rate to an average of 38.7%, while it resulted in a survival rate of 51-78.5% for transgenic lines. After cold acclimation, transgenic Arabidopsis plants overexpressing either LpIRI-a or LpIRI-b gene exhibited a consistent reduction in freezing-induced ion leakage at -8, -9, and -10 degrees C. Furthermore, the induced expression of the LpIRI-a and LpIRI-b proteins in transgenic E. coli enhanced the freezing tolerance in host cells. Our results suggest that IRI proteins play an important role in freezing tolerance in plants.

Lodging resistance is a key objective in pea breeding programs. Implementation of marker-assisted selection (MAS) in early generations could significantly enhance the efficiency of the breeding process compared with conventional selection in the F 3 or later generations. The objective of this research was to evaluate the effectiveness of MAS for lodging resistance using a combination of a coupling-phase linked marker A001 and a repulsion-phase linked marker A004 in F 2 generation field pea (Pisum sativum L.). Eight F 2 populations consisting of 680 plants were scored for the markers. A total of 402 F 3 families derived from MAS and 187 F 3 families from unselected populations were evaluated for lodging reaction under field conditions. The lowest lodging scores for each population were obtained from plants with the combination of A001 marker presence and A004 marker absence. A higher proportion of lodging resistant F 3 families was obtained from this marker combination as compared with phenotypic selection in the F 3 generation. MAS was less expensive than phenotypic selection in the field. Thus, A001 and A004 are useful for MAS for lodging resistance in early generation pea breeding populations.

To address the data ‘islandization’ issue in the statistical field and to take advantage of the opportunity of the Statistical Cloud construction, the National Bureau of Statistics of China (NBS) started adopting the concept of a “data middle platform” for data resource planning. With it, NBS aims to build a comprehensive data capability platform that includes data collection and exchange; data sharing and integration; data organizing and processing; data modeling and analyses; data management and governance; and data service and application. The statistical data middle platform provides the basic capability for data application support. It also enables data to form a closed loop between the data middle platform and the business system, and eventually realizes the ‘servitization’ of statistical data that meets internal and societal requirements. As a new innovative development, the statistical data middle platform will not only solve the long-standing data island problem of NBS but will also provide a basic guarantee for greater use of the data potential, and thus will help official statistics to transform from statistical analysis to predictive analysis, from single-domain to cross-domain, from passive analysis to active analysis, and from non-real-time to real-time analysis. The paper was prepared under the kind mentorship of Ronald Jansen, Assistant Director and Chief of Data Innovation at the UN Statistics Division in New York.

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