MARKET DRIVEN MOBILE GAMING TAXONOMY

This discourse describes a mobile game taxonomy that classifies mobile games into various main genres (e.g., action games, arcade games, role-playing games, music games, etc.) and further classifies the mobile games into various subgenres. For example, mobile games that are categorized as adventure games may be further classified into text adventure games, graphic adventure games, visual novel games, interactive movie games, etc. The main genre and/or the subgenre of a mobile game may be identified based on one or more game elements (e.g., game themes, game art styles, etc.) contained in the mobile game. Based on the identified main genre and/or the subgenre of the mobile game, a market analysis for the mobile game may be generated. For example, a market analysis for a particular mobile game may include a subgenre rank for the mobile game, suggested game elements for the mobile game, and a potential player profile for the mobile game

Transcriptional co-activator protein p100 interacts with snRNP proteins and facilitates the assembly of the spliceosome

Transcription and pre-mRNA splicing are the key nuclear processes in eukaryotic gene expression, and identification of factors common to both processes has suggested that they are functionally coordinated. p100 protein has been shown to function as a transcriptional co-activator for several transcription factors. p100 consists of staphylococcal nuclease (SN)-like and Tudor-SN (TSN) domains of which the SN-like domains have been shown to function in transcription, but the function of TSN domain has remained elusive. Here we identified interaction between p100 and small nuclear ribonucleoproteins (snRNP) that function in pre-mRNA splicing. The TSN domain of p100 specifically interacts with components of the U5 snRNP, but also with the other spliceosomal snRNPs. In vitro splicing assays revealed that the purified p100, and specifically the TSN domain of p100, accelerates the kinetics of the spliceosome assembly, particularly the formation of complex A, and the transition from complex A to B. Consistently, the p100 protein, as well as the separated TSN domain, enhanced the kinetics of the first step of splicing in an in vitro splicing assay in dose-dependent manner. Thus our results suggest that p100 protein is a novel dual function regulator of gene expression that participates via distinct domains in both transcription and splicing

Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method

The measured frequency response functions (FRFs) in the modal test are usually contaminated with noise that significantly affects the modal parameter identification. In this paper, a modal peak-based Hankel-SVD (MPHSVD) method is proposed to eliminate the noise contaminated in the measured FRFs in order to improve the accuracy of the identification of modal parameters. This method is divided into four steps. Firstly, the measured FRF signal is transferred to the impulse response function (IRF), and the Hankel-SVD method that works better in the time domain rather than in the frequency domain is further applied for the decomposition of component signals. Secondly, the iteration of the component signal accumulation is conducted to select the component signals that cover the concerned modal features, but some component signals of the residue noise may also be selected. Thirdly, another iteration considering the narrow frequency bands near the modal peak frequencies is conducted to further eliminate the residue noise and get the noise-reduced FRF signal. Finally, the modal identification method is conducted on the noise-reduced FRF to extract the modal parameters. A simulation of the FRF of a flat plate artificially contaminated with the random Gaussian noise and the random harmonic noise is implemented to verify the proposed method. Afterwards, a modal test of a flat plate under the high-temperature condition was undertaken using scanning laser Doppler vibrometry (SLDV). The noise reduction and modal parameter identification were exploited to the measured FRFs. Results show that the reconstructed FRFs retained all of the modal features we concerned about after the noise elimination, and the modal parameters are precisely identified. It demonstrates the superiority and effectiveness of the approach