pubs_peer_reviewed.bib

@article{shen2022-ChiSquareTest,
  title = {The {{Chi-Square Test}} of {{Distance Correlation}}},
  author = {Shen, Cencheng and Panda, Sambit and Vogelstein, Joshua T.},
  date = {2022-01-02},
  journaltitle = {Journal of Computational and Graphical Statistics},
  volume = {31},
  number = {1},
  eprint = {35707063},
  eprinttype = {pmid},
  pages = {254--262},
  publisher = {{Taylor \& Francis}},
  issn = {1061-8600},
  doi = {10.1080/10618600.2021.1938585},
  url = {https://doi.org/10.1080/10618600.2021.1938585},
  abstract = {Distance correlation has gained much recent attention in the data science community: the sample statistic is straightforward to compute and asymptotically equals zero if and only if independence, making it an ideal choice to discover any type of dependency structure given sufficient sample size. One major bottleneck is the testing process: because the null distribution of distance correlation depends on the underlying random variables and metric choice, it typically requires a permutation test to estimate the null and compute the p-value, which is very costly for large amount of data. To overcome the difficulty, in this article, we propose a chi-squared test for distance correlation. Method-wise, the chi-squared test is nonparametric, extremely fast, and applicable to bias-corrected distance correlation using any strong negative type metric or characteristic kernel. The test exhibits a similar testing power as the standard permutation test, and can be used for K-sample and partial testing. Theory-wise, we show that the underlying chi-squared distribution well approximates and dominates the limiting null distribution in upper tail, prove the chi-squared test can be valid and universally consistent for testing independence, and establish a testing power inequality with respect to the permutation test. Supplementary files for this article are available online.},
  annotation = {\_eprint: https://doi.org/10.1080/10618600.2021.1938585}
}

@article{wilson2018-SelectiveMechanically,
  title = {Selective and {{Mechanically Robust Sensors}} for {{Electrochemical Measurements}} of {{Real-Time Hydrogen Peroxide Dynamics}} in {{Vivo}}},
  author = {Wilson, Leslie R. and Panda, Sambit and Schmidt, Andreas C. and Sombers, Leslie A.},
  date = {2018-01-02},
  journaltitle = {Analytical Chemistry},
  shortjournal = {Anal. Chem.},
  volume = {90},
  number = {1},
  pages = {888--895},
  publisher = {{American Chemical Society}},
  issn = {0003-2700},
  doi = {10.1021/acs.analchem.7b03770},
  url = {https://doi.org/10.1021/acs.analchem.7b03770},
  abstract = {Hydrogen peroxide (H2O2) is an endogenous molecule that plays several important roles in brain function: it is generated in cellular respiration, serves as a modulator of dopaminergic signaling, and its presence can indicate the upstream production of more aggressive reactive oxygen species (ROS). H2O2 has been implicated in several neurodegenerative diseases, including Parkinson’s disease (PD), creating a critical need to identify mechanisms by which H2O2 modulates cellular processes in general and how it affects the dopaminergic nigrostriatal pathway, in particular. Furthermore, there is broad interest in selective electrochemical quantification of H2O2, because it is often enzymatically generated at biosensors as a reporter for the presence of nonelectroactive target molecules. H2O2 fluctuations can be monitored in real time using fast-scan cyclic voltammetry (FSCV) coupled with carbon-fiber microelectrodes. However, selective identification is a critical issue when working in the presence of other molecules that generate similar voltammograms, such as adenosine and histamine. We have addressed this problem by fabricating a robust, H2O2-selective electrode. 1,3-Phenylenediamine (mPD) was electrodeposited on a carbon-fiber microelectrode to create a size-exclusion membrane, rendering the electrode sensitive to H2O2 fluctuations and pH shifts but not to other commonly studied neurochemicals. The electrodes are described and characterized herein. The data demonstrate that this technology can be used to ensure the selective detection of H2O2, enabling confident characterization of the role this molecule plays in normal physiological function as well as in the progression of PD and other neuropathies involving oxidative stress.}
}