The sugar content of Solanum lycopersicum (tomato) fruit is a primary determinant of taste and quality. Cultivated tomato fruit are characterized by near-equimolar levels of the hexoses glucose and fructose, derived from the hydrolysis of translocated sucrose. As fructose is perceived as approximately twice as sweet as glucose, increasing its concentration at the expense of glucose can improve tomato fruit taste. Introgressions of the Fgr allele from the wild species Solanum habrochaites (LA1777) into cultivated tomato increased the fructose-to-glucose ratio of the ripe fruit by reducing glucose levels and concomitantly increasing fructose levels. In order to identify the function of the Fgr gene, we combined a fine-mapping strategy with RNAseq differential expression analysis of near-isogenic tomato lines. The results indicated that a SWEET protein was strongly upregulated in the lines with a high fructose-to-glucose ratio. Overexpressing the SWEET protein in transgenic tomato plants dramatically reduced the glucose levels and increased the fructose : glucose ratio in the developing fruit, thereby proving the function of the protein. The SWEET protein was localized to the plasma membrane and expression of the SlFgr gene in a yeast line lacking native hexose transporters complemented growth with glucose, but not with fructose. These results indicate that the SlFgr gene encodes a plasma membrane-localized glucose efflux transporter of the SWEET family, the overexpression of which reduces glucose levels and may allow for increased fructose levels. This article identifies the function of the tomato Fgr gene as a SWEET transporter, the upregulation of which leads to a modified sugar accumulation pattern in the fleshy fruit. The results point to the potential of the inedible wild species to improve fruit sugar accumulation via sugar transport mechanisms.

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Online social networks (OSNs) are abused by cyber criminals for various malicious activities. One of the most effective approaches for detecting malicious activity in OSNs involves the use of social network honeypotsartificial profiles that are deliberately planted within OSNs in order to attract abusers. Honeypot profiles have been used in detecting spammers, potential cyber attackers, and advanced attackers. Therefore, there is a growing need for the ability to reliably generate realistic artificial honeypot profiles in OSNs. In this research we present 'ProfileGen' -a method for the automated generation of profiles for professional social networks, giving particular attention to producing realistic education and employment records. 'ProfileGen' creates honeypot profiles that are similar to actual data by extrapolating the characteristics and properties of real data items. Evaluation by 70 domain experts confirms the method's ability to generate realistic artificial profiles that are indistinguishable from real profiles, demonstrating that our method can be applied to generate realistic artificial profiles for a wide range of applications.