A molecular framework for lc controlled locule development of the floral meristem in tomato

Malformed tomato fruit with multiple locules is a common physiological disorder that significantly affects the quality of tomatoes. Research has shown that the occurrence of malformed fruit in tomatoes is closely linked to the number of locules, and two key QTLs, lc and fas, are involved in controlling this trait. It has been observed that lc has a relatively weaker effect on increasing locule number, which is associated with two SNPs in the CArG repressor element downstream of the SlWUS. However, the precise molecular mechanism underlying lc is not yet fully understood. In this study, we investigated the role of lc in tomato locule development. We found that the number of floral organs and fruit locules significantly increased in tomato lc knockout mutants. Additionally, these mutants showed higher expression levels of the SlWUS during carpel formation. Through cDNA library construction and yeast one-hybrid screening, we identified the MADS-box transcription factor SlSEP3, which was found to bind to lc. Furthermore, we observed an increase in floral organs and fruit locules similar to the lcCR plant on SlSEP3 silencing plants. However, it should be noted that the lc site is located after the 3′ untranslated region (UTR) of SlWUS in the tomato genome. As a result, SlSEP3 may not be able to exert regulatory functions on the promoter of the gene like other transcription factors. In the yeast two-hybrid assay, we found that several histone deacetylases (SlHDA1, SlHDA3, SlHDA4, SlHDA5, SlHDA6, SlHDA7, and SlHDA8) can interact with SlSEP3. This indicated that SlSEP3 can recruit these proteins to repress nucleosome relaxation, thereby inhibiting SlWUS transcription and affecting the number of locules in tomato fruit. Therefore, our findings reveal a new mechanism for lc playing a significant role in the genetic pathway regulating tomato locule development

A state of the art survey of data mining-based fraud detection and credit scoring

Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches

A state of the art survey of data mining-based fraud detection and credit scoring

Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches

Analysis of <i>YUC</i> and <i>TAA/TAR</i> Gene Families in Tomato

Auxin is a vital phytohormone, but its synthesis pathway is poorly understood. This study used bioinformatic analysis to identify and analyze the gene family members that encode tomato auxin biosynthesis. The FZY gene family members encoding flavin-containing monooxygenases were retrieved from the tomato genome database. DNAMAN analysis revealed nine genes within the landmark domain WL(I/V)VATGENAE, between the FAD and NADPH domains. Phylogenetic analysis showed that the FZY gene family in tomato is closely related to the YUC gene family in Arabidopsis thaliana. A qRT-PCR showed that SlFZY2, SlFZY3, SlFZY4-1, and SlFZY5 were highly expressed in tomato flower organs. The analysis of promoter cis-acting elements revealed light-responsive elements in the promoters of all nine members in tomato, indicating their sensitivity to light signals. Furthermore, the promoters of SlFZY4-2, SlFZY5, and SlFZY7 contain low-temperature-responsive elements. This study demonstrated that SlTAA5 expression was 2.22 times that of SlTAA3 in the roots, and SlTAA3 expression in the pistils was 83.58 times that in the stamens during the tomato flowering stage. Therefore, various members of the tomato FZY gene family are involved in regulating the development of tomato floral organs and are responsive to abiotic stresses, such as low temperature and weak light