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  1. ; ; ; ; ( , Nature Communications)
    Abstract High-resolution reconstruction of spatial chromosome organizations from chromatin contact maps is highly demanded, but is hindered by extensive pairwise constraints, substantial missing data, and limited resolution and cell-type availabilities. Here, we present FLAMINGO, a computational method that addresses these challenges by compressing inter-dependent Hi-C interactions to delineate the underlying low-rank structures in 3D space, based on the low-rank matrix completion technique. FLAMINGO successfully generates 5 kb- and 1 kb-resolution spatial conformations for all chromosomes in the human genome across multiple cell-types, the largest resources to date. Compared to other methods using various experimental metrics, FLAMINGO consistently demonstrates superior accuracy in recapitulatingmore »observed structures with raises in scalability by orders of magnitude. The reconstructed 3D structures efficiently facilitate discoveries of higher-order multi-way interactions, imply biological interpretations of long-range QTLs, reveal geometrical properties of chromatin, and provide high-resolution references to understand structural variabilities. Importantly, FLAMINGO achieves robust predictions against high rates of missing data and significantly boosts 3D structure resolutions. Moreover, FLAMINGO shows vigorous cross cell-type structure predictions that capture cell-type specific spatial configurations via integration of 1D epigenomic signals. FLAMINGO can be widely applied to large-scale chromatin contact maps and expand high-resolution spatial genome conformations for diverse cell-types.« less
    Free, publicly-accessible full text available December 1, 2023
  2. ; ; ; ; ( , Nature Communications)
    Abstract During development, neural progenitors are temporally patterned to sequentially generate a variety of neural types. In Drosophila neural progenitors called neuroblasts, temporal patterning is regulated by cascades of Temporal Transcription Factors (TTFs). However, known TTFs were mostly identified through candidate approaches and may not be complete. In addition, many fundamental questions remain concerning the TTF cascade initiation, progression, and termination. In this work, we use single-cell RNA sequencing of Drosophila medulla neuroblasts of all ages to identify a list of previously unknown TTFs, and experimentally characterize their roles in temporal patterning and neuronal specification. Our study reveals a comprehensivemore »temporal gene network that patterns medulla neuroblasts from start to end. Furthermore, the speed of the cascade progression is regulated by Lola transcription factors expressed in all medulla neuroblasts. Our comprehensive study of the medulla neuroblast temporal cascade illustrates mechanisms that may be conserved in the temporal patterning of neural progenitors.« less
    Free, publicly-accessible full text available December 1, 2023
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Nature Communications)
    Abstract Digital light processing bioprinting favors biofabrication of tissues with improved structural complexity. However, soft-tissue fabrication with this method remains a challenge to balance the physical performances of the bioinks for high-fidelity bioprinting and suitable microenvironments for the encapsulated cells to thrive. Here, we propose a molecular cleavage approach, where hyaluronic acid methacrylate (HAMA) is mixed with gelatin methacryloyl to achieve high-performance bioprinting, followed by selectively enzymatic digestion of HAMA, resulting in tissue-matching mechanical properties without losing the structural complexity and fidelity. Our method allows cellular morphological and functional improvements across multiple bioprinted tissue types featuring a wide range ofmore »mechanical stiffness, from the muscles to the brain, the softest organ of the human body. This platform endows us to biofabricate mechanically precisely tunable constructs to meet the biological function requirements of target tissues, potentially paving the way for broad applications in tissue and tissue model engineering.« less
    Free, publicly-accessible full text available December 1, 2023
  4. ( , ICSE Workshop on Automation of Software Test)
    Regression testing - rerunning tests at each code version to detect newly-broken functionality - is important and widely practiced. But regression testing is costly due to the large number of tests and high frequency of code changes. Regression test selection (RTS) optimizes regression testing by rerunning only a subset of tests that can be affected by code changes. Researchers showed that RTS based on dynamic and static program analysis can save substantial testing time for (medium-sized) open-source projects. Simultaneously, practitioners showed that RTS based on machine learning (ML) is lightweight and works well on very large software repositories, e.g., inmore »Facebook’s monorepository. We combine analysis-based RTS and ML-based RTS by using ML-based RTS to choose a subset of tests selected by analysis-based RTS. To do so, we first design several novel ML-based RTS techniques that leverage mutation analysis to obtain a training set for learning the impact of code changes on test outcomes. Then, we empirically evaluate, using 10 projects, the benefits of combining various ML models with analysis-based RTS. We also compare combining the techniques with using each technique individually. Combining ML-based RTS with two analysis-based RTS techniques - Ekstazi and STARTS - selects 25.34% and 21.44% fewer tests.« less
    Free, publicly-accessible full text available April 1, 2023
  5. ; ; ( , IEEE Robotics and Automation Letters)
    Free, publicly-accessible full text available April 1, 2023
  6. ; ; ; ; ; ; ; ; ; ; et al ( , Advanced Materials)
    Free, publicly-accessible full text available April 1, 2023
  7. ; ( , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
    Free, publicly-accessible full text available September 27, 2022
  8. ; ; ; ; ; ; ; ; ; ; et al ( , Biofabrication)
    Abstract Thrombosis in the circulation system can lead to major myocardial infarction and cardiovascular deaths. Understanding thrombosis formation is necessary for developing safe and effective treatments. In this work, using digital light processing (DLP)-based 3D printing, we fabricated sophisticated in vitro models of blood vessels with internal microchannels that can be used for thrombosis studies. In this regard, photoacoustic microscopy (PAM) offers a unique advantage for label-free visualization of the 3D-printed vessel models, with large penetration depth and functional sensitivity. We compared the imaging performances of two PAM implementations: optical-resolution PAM and acoustic-resolution PAM, and investigated 3D-printed vessel structures withmore »different patterns of microchannels. Our results show that PAM can provide clear microchannel structures at depths up to 3.6 mm. We further quantified the blood oxygenation in the 3D-printed vascular models, showing that thrombi had lower oxygenation than the normal blood. We expect that PAM can find broad applications in 3D printing and bioprinting for in vitro studies of various vascular and other diseases.« less
    Free, publicly-accessible full text available January 24, 2023
  9. ; ; ; ; ; ; ; ; ; ( , Advanced Materials)
    Free, publicly-accessible full text available January 1, 2023
  10. ; ; ; ; ; ; ; ; ( , Geophysical Research Letters)
    Free, publicly-accessible full text available December 16, 2022