Liang He

Two Saccharomyces cerevisiae genes, CIN8 and KIP1 (a.k.a. CIN9), were identified by their requirement for normal chromosome segregation. Both genes encode polypeptides related to the heavy chain of the microtubule-based force-generating enzyme kinesin. Cin8p was found to be required for pole separation during mitotic spindle assembly at 37 degrees C, although overproduced Kip1p could substitute. At lower temperatures, the activity of at least one of these proteins was required for cell viability, indicating that they perform an essential but redundant function. Cin8p was observed to be a component of the mitotic spindle, colocalizing with the microtubules that lie between the poles. Taken together, these findings suggest that these proteins interact with spindle microtubules to produce an outwardly directed force acting upon the poles.

Kinesin-related Cin8p is the most important spindle-pole-separating motor in Saccharomyces cerevisiae but is not essential for cell viability. We identified 20 genes whose products are specifically required by cell deficient for Cin8p. All are associated with mitotic roles and represent at least four different functional pathways. These include genes whose products act in two spindle motor pathways that overlap in function with Cin8p, the kinesin-related Kip1p pathway and the cytoplasmic dynein pathway. In addition, genes required for mitotic spindle checkpoint function and for normal microtubule stability were recovered. Mutant alleles of eight genes caused phenotypes similar to dyn1 (encodes the dynein heavy chain), including a spindle-positioning defect. We provide evidence that the products of these genes function in concept with dynein. Among the dynein pathway gene products, we found homologues of the cytoplasmic dynein intermediate chain, the p150Glued subunit of the dynactin complex, and human LIS-1, required for normal brain development. These findings illustrate the complex cellular interactions exhibited by Cin8p, a member of a conserved spindle motor family.

The kinesin-related products of the CIN8 and KIP1 genes of Saccharomyces cerevisiae redundantly perform an essential function in mitosis. The action of either gene-product is required for an outwardly directed force that acts upon the spindle poles. We have selected mutations that suppress the temperature-sensitivity of a cin8-temperature-sensitive kip1-delta strain. The extragenic suppressors analyzed were all found to be alleles of the KAR3 gene. KAR3 encodes a distinct kinesin-related protein whose action antagonizes Cin8p/Kip1p function. All seven alleles analyzed were altered within the region of KAR3 that encodes the putative force-generating (or "motor") domain. These mutations also suppressed the inviability associated with the cin8-delta kip1-delta genotype, a property not shared by a deletion of KAR3. Other properties of the suppressing alleles revealed that they were not null for function. Six of the seven were unaffected for the essential karyogamy and meiosis properties of KAR3 and the seventh was dominant for the suppressing trait. Our findings suggest that despite an antagonistic relationship between Cin8p/Kip1p and Kar3p, aspects of their mitotic roles may be similar.

In this paper, we propose Personalized Markov Embedding (PME), a next-song recommendation strategy for online karaoke users. By modeling the sequential singing behavior, we first embed songs and users into a Euclidean space in which distances between songs and users reflect the strength of their relationships. Then, given each user's last song, we can generate personalized recommendations by ranking the candidate songs according to the embedding. Moreover, PME can be trained without any requirement of content information. Finally, we perform an experimental evaluation on a real world data set provided by ihou.com which is an online karaoke website launched by iFLYTEK, and the results clearly demonstrate the effectiveness of PME.

The majority of current research in Automated Essay Scoring (AES) focuses on prompt-specific scoring of either the overall quality of an essay or the quality with regards to certain traits. In real-world applications obtaining labelled data for a target essay prompt is often expensive or unfeasible, requiring the AES system to be able to perform well when predicting scores for essays from unseen prompts. As a result, some recent research has been dedicated to cross-prompt AES. However, this line of research has thus far only been concerned with holistic, overall scoring, with no exploration into the scoring of different traits. As users of AES systems often require feedback with regards to different aspects of their writing, trait scoring is a necessary component of an effective AES system. Therefore, to address this need, we introduce a new task named Automated Cross-prompt Scoring of Essay Traits, which requires the model to be trained solely on non-target-prompt essays and to predict the holistic, overall score as well as scores for a number of specific traits for target-prompt essays. This task challenges the model's ability to generalize in order to score essays from a novel domain as well as its ability to represent the quality of essays from multiple different aspects. In addition, we introduce a new, innovative approach which builds on top of a state-of-the-art method for cross-prompt AES. Our method utilizes a trait-attention mechanism and a multi-task architecture that leverages the relationships between each trait to simultaneously predict the overall score and the score of each individual trait. We conduct extensive experiments on the widely used ASAP and ASAP++ datasets and demonstrate that our approach is able to outperform leading prompt-specific trait scoring and cross-prompt AES methods.