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Stem trichomes and seed fibers originate from epidermal cells and partially share a regulatory pathway at the molecular level. In Gossypium barbadense, two insertions of a Ty1 long-terminal repeat-retrotransposon [transposable element TE1 and TE2] in a homeodomain-leucine zipper gene (HD1) result in glabrous stems. The primers used to identify the TE insertions in G. barbadense were applied to screen for the same events in 81 modern G. hirsutum varieties and 31 wild races. Three wild races were found carrying the same TEs as G. barbadense. However, the TE insertions in two of these wild races occurred at different sites (4th exon), therefore, named TE3, while the TE in the other wild race occurred at the same site as TE2. An RNA sequencing and qRT-PCR analysis indicated that the loss of HD1 function was caused by the TE insertion. Genetic mapping revealed a strong association between glabrous stems and TE3 insertions, confirming that HD1 is a critical gene for stem trichome initiation in G. hirsutum, as in G. barbadense. Using the long-terminal repeat sequence as a query to search against the Texas Marker-1 reference genome sequence, we found that the TE occurred after tetraploid cotton formation and evolved at different rates in G. hirsutum and G. barbadense. Interestingly, at least three independent insertion events of the same retrotransposon occurred preferentially in the A sub-genome's HD1 gene, but not in the D sub-genome of G. hirsutum or G. barbadense, suggesting that an unknown TE insertion mechanism and resultant gene function changes may have hastened cotton speciation. [ABSTRACT FROM AUTHOR]

Online prediction is a process that repeatedly predicts the next element in the coming period from a sequence of given previous elements. This process has a broad range of applications in various areas, such as medical, streaming media, and finance. The greatest challenge for online prediction is that the sequence data may not have explicit features because the data is frequently updated, which means good predictions are difficult to maintain. One of the popular solutions is to make the prediction with expert advice, and the challenge is to pick the right experts with minimum cumulative loss. In this research, we use the forex trading prediction, which is a good example for online prediction, as a case study. We also propose an improved expert selection model to select a good set of forex experts by learning previously observed sequences. Our model considers not only the average mistakes made by experts, but also the average profit earned by experts, to achieve a better performance, particularly in terms of financial profit. We demonstrate the merits of our model on two real major currency pairs corpora with extensive experiments. [ABSTRACT FROM AUTHOR]

In many types of databases, such as a science bibliography database, the name attribute is the most commonly used identifier to recognize entities. However, names are frequently ambiguous and not always unique, thereby causing problems in various fields. Name disambiguation is a data management task that aims to properly distinguish different entities that share the same name, particularly for large databases such as digital libraries, because the information that can be used to identify author’s name is limited. In digital libraries, the issue of ambiguous author names occurs due to the existence of multiple authors with the same name or different name variations for the same author. Most previous works conducted to solve this issue frequently used hierarchical clustering approaches based on information within citation records, e.g., co-authors and publication titles. In the present study, we propose a multiple layers name disambiguation framework that is not only applicable to digital libraries but can also be easily extended to other applications. Our framework adopts a dynamic clustering mechanism to minimize clustering errors. We evaluated our approach on real world corpora, and favorable experiment results indicated that our proposed framework was feasible. [ABSTRACT FROM AUTHOR]

The adverse drug event (ADE) is an unexpected and harmful consequence of drug usege. Identifying the association between the use of drugs and adverse events from biomedical literature can contribute a lot to drug safety supervision. Such identification can not only assist drug safety monitoring, but also correct known dependencies among events. In this paper,we propose a novel approach based on graph algorithm to detect adverse drug events(GA-ADE). In our approach, we first construct a graph using candidate ADE extracted from biomedical literature. We then propose a method to select important vertices from the graph as core vertices, and design a Personal Rank algorithm using the core vertices for clustering to build subgraphs. Lastly, the correlation between the drug and the event is calculated based on the subgraphs. Experiments show that our approach is feasible. [ABSTRACT FROM AUTHOR]