Existing regionalization methods tend to be either spatial attribute- or spatial interaction-based, while real-world tasks usually involve both considerations to satisfy multiple objectives simultaneously. In this research, we propose Multilayer Network Community Detection and Kernel Extension (MNCD-KE), a two-step regionalization framework, as a feasible solution for such tasks. First, spatial attributes are embedded into attributes of nodes in a spatial interaction-defined multilayer network, and the kernel and marginal parts of the regions are determined by giving the membership value of the regionalization units to network communities. Second, the final result is obtained through a kernel extension process considering geographical constraints, including spatial contiguity, size balance, morphological regularity, and existing boundary consistency of the regions. Empirical experiments show that the proposed method yields outcomes that, in maintaining comparable performances with most baseline algorithms with either ‘attribute’ or ‘interaction’ objectives as measured by the respective criteria, simultaneously meet the dual objectives with results intuitively comprehensible. Its low computing costs and parameter adjustment flexibility make the proposed framework a convenient approach for real-world multi-objective regionalization tasks. We conclude the research with discussions on the boundary conditions for the framework to work and their relevance to city science theories, along with practical implications.

Genome-wide association studies (GWAS) using longitudinal phenotypes collected over time is appealing due to the improvement of power. However, computation burden has been a challenge because of the complex algorithms for modeling the longitudinal data. Approximation methods based on empirical Bayesian estimates (EBEs) from mixed-effects modeling have been developed to expedite the analysis. However, our analysis demonstrated that bias in both association test and estimation for the existing EBE-based methods remains an issue. We propose an incredibly fast and unbiased method (simultaneous correction for EBE, SCEBE) that can correct the bias in the naive EBE approach and provide unbiased P-values and estimates of effect size. Through application to Alzheimer's Disease Neuroimaging Initiative data with 6 414 695 single nucleotide polymorphisms, we demonstrated that SCEBE can efficiently perform large-scale GWAS with longitudinal outcomes, providing nearly 10 000 times improvement of computational efficiency and shortening the computation time from months to minutes. The SCEBE package and the example datasets are available at https://github.com/Myuan2019/SCEBE.

MOTIVATION: Estimating haplotype frequencies from genotype data plays an important role in genetic analysis. In silico methods are usually computationally involved since phase information is not available. Due to tight linkage disequilibrium and low recombination rates, the number of haplotypes observed in human populations is far less than all the possibilities. This motivates us to solve the estimation problem by maximizing the sparsity of existing haplotypes. Here, we propose a new algorithm by applying the compressive sensing (CS) theory in the field of signal processing, compressive sensing haplotype inference (CSHAP), to solve the sparse representation of haplotype frequencies based on allele frequencies and between-allele co-variances. RESULTS: Our proposed approach can handle both individual genotype data and pooled DNA data with hundreds of loci. The CSHAP exhibits the same accuracy compared with the state-of-the-art methods, but runs several orders of magnitude faster. CSHAP can also handle with missing genotype data imputations efficiently. AVAILABILITY AND IMPLEMENTATION: The CSHAP is implemented in R, the source code and the testing datasets are available at http://home.ustc.edu.cn/∼zhouys/CSHAP/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Musical auditory habilitation is an essential process in adapting cochlear implant recipients to the musical hearing context provided by cochlear implants. However, due to the cost and time limitation, it is impossible for hearing healthcare professionals to provide intensive and extensive musical auditory habilitation for every cochlear implant recipient. In order to provide an efficient and cost-effective musical auditory training for children with cochlear implants, we designed and developed MObile Games with Auditory Training (MOGAT) on off-the-shelf mobile devices. MOGAT includes three intuitive and interesting mobile games for training pitch perception and production, and a cloud-based web service for music therapists to support and evaluate individual habilitation. We demonstrate MOGAT for enhancing musical habilitation for children with cochlear implants.

Cochlear implants have improved the lives of tens of thousands of the hearing impaired by providing sufficient auditory perception for speech, but these devices are far from satisfactory for music perception. Many cochlear implant recipients, especially pre-lingually deafened children, have difficulty recognizing and producing specific pitches. To improve musical auditory habilitation for children post cochlear implantation, we developed MOGAT: MObile Games with Auditory Training. The system includes three musical games built with off-the-shelf mobile devices to train their pitch perception and intonation skills respectively, and a cloud-based web service which allows music therapists to monitor and design individual training for children. The design of the games and web service was informed by a pilot survey (N=60 children). To ensure widespread use with low-cost mobile devices, we minimized the computation load while retaining highly accurate audio analysis. A 6-week user study (N=15 children) showed that the music habilitation with MOGAT was intuitive, enjoyable and motivating. It has improved most children's pitch discrimination and production, and several children's improvement was statistically significant (p<0.05).

Composition, listening, and performance are essential activities in classroom music education, yet conventional music classes impose unnecessary limitations on students' ability to develop these skills. Based on in-depth fieldwork and a user-centered design approach, we created MOGCLASS, a multimodal collaborative music environment that enhances students' musical experience and improves teachers' management of the classroom.

We introduce MOGCLASS: a system of networked mobile devices to amplify and extend children's capabilities to perceive, perform and produce music collaboratively in classroom context. MOGCLASS includes various features for students to enhance their motivation, interest, and collaboration in music class. It provides a wide-ranging palette of easy-to-use musical instruments for students to choose from, and supports both collaborative silent practice with headphones, and collaborative performance with loudspeakers. To facilitate classroom management, the teacher's interface is used to control students' activities. Our evaluation results indicate that MOGCLASS is effective in increasing students' motivation in learning music and in supporting teachers' classroom management

The computational power and sensory capabilities of mobile devices are increasing dramatically these days, rendering them suitable for real-time sound synthesis and various musical expressions. In this paper, we demonstrate a novel mobile music making system which leverages the ubiquity, ultra-mobility, and multi-modality of mobile devices (iPod touch) for people to create and compose music collaboratively. Unlike the conventional music making applications which generate the music on a single mobile device with a preset sound and interface, our system allows several players in a group to be connected together through wireless LAN network, creating music with different sounds and interfaces. Finally, the performance can be recorded as a single music file and played back in the future. The paper also shows some application scenarios for this collaborative music making system in future research.