Woodland strawberry WRKY71 acts as a promoter of flowering via a transcriptional regulatory cascade

The WRKY proteins are a large family of transcription factors that play important roles in stress responses and plant development.However,the roles of most WRKYs in strawberry are not well known.In this study,FvWRKY71 was isolated from the woodland strawberry‘Ruegen’.FvWRKY71 was highly expressed in the shoot apex and red fruit.Subcellular localization analysis showed that FvWRKY71 was located in the nucleus.Transactivation analysis showed that FvWRKY71 presented transcriptional activation activity in yeast.Overexpression of FvWRKY71 in Arabidopsis and woodland strawberry revealed early flowering in the transgenic plants compared with the wild-type control.Gene expression analysis indicated that the transcript levels of the flowering time and development integrator genes AP1,LFY,FT,AGL42,FUL,FPF1,SEP1,SEP2,and SEP3 were increased in FvWRKY71-overexpressing Arabidopsis and strawberry plants compared with the wild-type controls,which may result in accelerated flowering in transgenic plants.Furthermore,FvWRKY71 was proven to directly bind to the W-boxes(TTGACT/C)of the FvFUL,FvSEP1,FvAGL42,FvLFY,and FvFPF1 promoters in vitro and in vivo.Taken together,our results reveal a transcriptional regulatory cascade of FvWRKY71 involved in promoting flowering in woodland strawberry.

Dual role of MdSND1 in the biosynthesis of lignin and in signal transduction in response to salt and osmotic stress in apple

Clarifying the stress signal transduction pathway would be helpful for understanding the abiotic stress resistance mechanism in apple(Malus×domestica Borkh.)and could assist in the development of new varieties with high stress tolerance by genetic engineering.The key NAC transcription factor SND1,which is involved in the lignin biosynthesis process in apple,was functionally analyzed.The results of the stress treatments indicated that MdSND1 could be induced by salt,mannitol and ABA.Compared with wild-type GL-3 plants,MdSND1-overexpressing apple plants with greater antioxidant capacity and lignin were more resistant to salt and simulated osmotic stress,while RNAi plants were more vulnerable.Additionally,molecular experiments confirmed that MdSND1 could regulate the biosynthesis of lignin by activating the transcription of MdMYB46/83.Moreover,genes known to be involved in the stress signal transduction pathway(MdAREB1A,MdAREB1B,MdDREB2A,MdRD29A,and MdRD22)were screened for their close correlations with the expression of MdSND1 and the response to salt and osmotic stress.Multiple verification tests further demonstrated that MdSND1 could directly bind to these gene promoters and activate their transcription.The above results revealed that MdSND1 is directly involved in the regulation of lignin biosynthesis and the signal transduction pathway involved in the response to both salt and osmotic stress in apple.

Functional identification of MdMYB5 involved in secondary cell wall formation in apple

Being the principal elements of secondary cell wall,cellulose and lignin both play a strengthening role in plant structures and stress resistance.However,little research has been done regarding the molecular mechanisms involved in the formation of cellulose and lignin in apple.In this study,in order to better understand the regulatory network in the formation of secondary cell wall,an R2R3 MYB transcriptional factor MdMYB5 was identified and explored.The subcellular localization experiments showed that MdMYB5 could function in the nucleus.Even though lignin and cellulose content,and the expression of their biosynthesis related genes decreased in the MdMYB5-RNAi apple,the ectopic overexpression of MdMYB5 promotes lignin and cellulose content in Arabidopsis,which contributes to the dwarf phenotype.At the same time,salt and osmotic stress affect MdMYB5-RNAi apple tissue cultures.Further transcriptional activation assays carried out demonstrated that MdMYB5 could be activated by MdMYB46 and MdSND1.In conclusion,MdMYB5 was required for the normal formation of secondary cell wall in apple and could be activated by the key regulatory factors MdMYB46 and MdSND1.

COLLABORATIVE DESIGN OF RFID SYSTEMS FOR MULTI-PURPOSE SUPPLY CHAIN APPLICATIONS

One of the key challenges for implementing RFID systems in supply chain management is the difficulty in economic justification. Such difficulty is further amplified by its public participation nature as multiple self-interest beneficiaries may receive diverse paybacks, and their incentives to join the system are difficult to align. This paper aims to address these problems by a collaborative design from two aspects. First, we propose to introduce a centralized planning mechanism in the chain to facilitate the participation, so that the cost of the overall system can be minimized. Second, we propose to analyze the multi-facet economic return from multi-purpose applications to achieve the full potential of RFID systems. To illustrate our approach, its application for inventory inaccuracy and product recall in RFID system is presented.

Item-level Forecasting for E-commerce Demand with High-dimensional Data Using a Two-stage Feature Selection Algorithm

With the rapid development of information technology and fast growth of Internet users,e-commerce nowadays is facing complex business environment and accumulating large-volume and highdimensional data.This brings two challenges for demand forecasting.First,e-merchants need to find appropriate approaches to leverage the large amount of data and extract forecast features to capture various factors affecting the demand.Second,they need to efficiently identify the most important features to improve the forecast accuracy and better understand the key drivers for demand changes.To solve these challenges,this study conducts a multi-dimensional feature engineering by constructing five feature categories including historical demand,price,page view,reviews,and competition for e-commerce demand forecasting on item-level.We then propose a two-stage random forest-based feature selection algorithm to effectively identify the important features from the high-dimensional feature set and avoid overfittlng.We test our proposed algorithm with a large-scale dataset from the largest e-commerce platform in China.The numerical results from 21,111 items and 109 million sales observations show that our proposed random forest-based forecasting framework with a two-stage feature selection algorithm delivers 11.58%,5.81%and 3.68%forecast accuracy improvement,compared with the Autoregressive Integrated Moving Average(ARIMA),Random Forecast,and Random Forecast with one-stage feature selection approach,respectively,which are widely used in literature and industry.This study provides a useful tool for the practitioners to forecast demands and sheds lights on the B2C e-commerce operations management.