All Publications

  • Extracting hierarchical features of cultural variation using network-based clustering EVOLUTIONARY HUMAN SCIENCES Liu, X., Rosenberg, N. A., Greenbaum, G. 2022; 4
  • Effects of cultural transmission of surnaming decisions on the sex ratio at birth. Theoretical population biology Liu, X., Feldman, M. W. 2021


    The patriarchal tradition of surnaming a child after its father in Han Chinese families may contribute to their preference for sons, a major cause of the abnormally high SRB (sex ratio at birth) in China. This high SRB can subsequently contribute to the marriage squeeze on males of marriageable age. Encouraging matrilineal surnaming has been proposed as a strategy that could potentially reduce son preference and help to adjust the imbalance in SRB. Here, we model factors that are likely to influence surnaming decisions, including cultural transmission of parents' surnaming decisions, the cultural value of a daughter, reward given to matrilineal surnaming, and awareness of current imbalance in SRB. Mathematical and computational analyses suggest that offering a significant reward and raising public awareness of the problems inherent in an excess of marriage-age males may overcome the son preference and reduce the male-biased SRB.

    View details for DOI 10.1016/j.tpb.2021.07.001

    View details for PubMedID 34358559

  • Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology. Clinical cancer research : an official journal of the American Association for Cancer Research Ye, Z., Price, R. L., Liu, X., Lin, J., Yang, Q., Sun, P., Wu, A. T., Wang, L., Han, R. H., Song, C., Yang, R., Gary, S. E., Mao, D. D., Wallendorf, M., Campian, J. L., Li, J., Dahiya, S., Kim, A. H., Song, S. V. 2020


    PURPOSE: Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted for examining GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in the clinical management of GBMs.EXPERIMENTAL DESIGN: We employ a novel Diffusion Histology Imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM.RESULTS: Gd-enhanced T1W or hyper-intense FLAIR failed to reflect the morphological complexity underlying tumor in GBM patients. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in glioblastoma specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0% and 93.4% accuracy, respectively.CONCLUSIONS: Our results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques for guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of glioblastoma.

    View details for DOI 10.1158/1078-0432.CCR-20-0736

    View details for PubMedID 32694155