skip to main content


Search for: All records

Creators/Authors contains: "Zhou, Min"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    A common issue for classification in scientific research and industry is the existence of imbalanced classes. When sample sizes of different classes are imbalanced in training data, naively implementing a classification method often leads to unsatisfactory prediction results on test data. Multiple resampling techniques have been proposed to address the class imbalance issues. Yet, there is no general guidance on when to use each technique. In this article, we provide a paradigm‐based review of the common resampling techniques for binary classification under imbalanced class sizes. The paradigms we consider include the classical paradigm that minimizes the overall classification error, the cost‐sensitive learning paradigm that minimizes a cost‐adjusted weighted type I and type II errors, and the Neyman–Pearson paradigm that minimizes the type II error subject to a type I error constraint. Under each paradigm, we investigate the combination of the resampling techniques and a few state‐of‐the‐art classification methods. For each pair of resampling techniques and classification methods, we use simulation studies and a real dataset on credit card fraud to study the performance under different evaluation metrics. From these extensive numerical experiments, we demonstrate under each classification paradigm, the complex dynamics among resampling techniques, base classification methods, evaluation metrics, and imbalance ratios. We also summarize a few takeaway messages regarding the choices of resampling techniques and base classification methods, which could be helpful for practitioners.

     
    more » « less
  2. Abstract

    Syncytial isopotentiality, resulting from a strong electrical coupling, emerges as a physiological mechanism that coordinates individual astrocytes to function as a highly efficient system in brain homeostasis. However, whether syncytial isopotentiality occurs selectively to certain brain regions or is universal to astrocytic networks remains unknown. Here, we have explored the correlation of syncytial isopotentiality with different astrocyte subtypes in various brain regions. Using a nonphysiological K+‐free/Na+electrode solution to depolarize a recorded astrocyte in situ, the existence of syncytial isopotentiality can be revealed: the recorded astrocyte's membrane potential remains at a quasi‐physiological level due to strong electrical coupling with neighboring astrocytes. Syncytial isopotentiality appears in Layer I of the motor, sensory, and visual cortical regions, where astrocytes are organized with comparable cell densities, interastrocytic distances, and the quantity of directly coupled neighbors. Second, though astrocytes vary in their cytoarchitecture in association with neuronal circuits from Layers I–VI, the established syncytial isopotentiality remains comparable among different layers in the visual cortex. Third, neurons and astrocytes are uniquely organized as barrels in Layer IV somatosensory cortex; interestingly, astrocytes both inside and outside of the barrels do electrically communicate with each other and also share syncytial isopotentiality. Fourth, syncytial isopotentiality appears in radial‐shaped Bergmann glia and velate astrocytes in the cerebellar cortex. Fifth, although fibrous astrocytes in white matter exhibit a distinct morphology, their network syncytial isopotentiality is comparable with protoplasmic astrocytes. Altogether, syncytial isopotentiality appears as a system‐wide electrical feature of astrocytic networks in the brain.

     
    more » « less
  3. Abstract

    Chloramphenicol acetyltransferases (CATs) were among the first antibiotic resistance enzymes identified and have long been studied as model enzymes for examining plasmid‐mediated antibiotic resistance. These enzymes acetylate the antibiotic chloramphenicol, which renders it incapable of inhibiting bacterial protein synthesis. CATs can be classified into different types: Type A CATs are known to be important for antibiotic resistance to chloramphenicol and fusidic acid. Type B CATs are often called xenobiotic acetyltransferases and adopt a similar structural fold to streptogramin acetyltransferases, which are known to be critical for streptogramin antibiotic resistance. Type C CATs have recently been identified and can also acetylate chloramphenicol, but their roles in antibiotic resistance are largely unknown. Here, we structurally and kinetically characterized threeVibrioCAT proteins from a nonpathogenic species (Aliivibrio fisheri) and two important human pathogens (Vibrio choleraeandVibrio vulnificus). We found all three proteins, including one in a superintegron (V. cholerae), acetylated chloramphenicol, but did not acetylate aminoglycosides or dalfopristin. We also determined the 3D crystal structures of these CATs alone and in complex with crystal violet and taurocholate. These compounds are known inhibitors of Type A CATs, but have not been explored in Type B and Type C CATs. Based on sequence, structure, and kinetic analysis, we concluded that theV. choleraeandV. vulnificusCATs belong to the Type B class and theA. fisheriCAT belongs to the Type C class. Ultimately, our results provide a framework for studying the evolution of antibiotic resistance gene acquisition and chloramphenicol acetylation inVibrioand other species.

     
    more » « less