I Got More Data, My Model is More Refined, but My Estimator is Getting Worse! Am I Just Dumb?

Possibly, but more likely you are merely a victim of conventional wisdom. More data or better models by no means guarantee better estimators (e.g., with a smaller mean squared error), when you are not following probabilistically principled methods such as MLE (for large samples) or Bayesian approaches. Estimating equations are particularly vulnerable in this regard, almost a necessary price for their robustness. These points will be demonstrated via common tasks of estimating regression parameters and correlations, under simple models such as bivariate normal and ARCH(1). Some general strategies for detecting and avoiding such pitfalls are suggested, including checking for self-efficiency (Meng, 1994; Statistical Science) and adopting a guiding working model. Using the example of estimating the autocorrelation $\rho$ under a stationary AR(1) model, we also demonstrate the interaction between model assumptions and observation structures in seeking additional information, as the sampling interval $s$ increases. Furthermore, for a given sample size, the optimal s for minimizing the asymptotic variance of $\hat{\rho}_{MLE}$is $s = 1$ if and only if $\rho^2 ≤ 1/3$; beyond that region the optimal s increases at the rate of $log ^{−1}(\rho^{−2})$ as $\rho$ approaches a unit root, as does the gain in efficiency relative to using $s = 1$. A practical implication of this result is that the so-called “non-informative” Jeffreys prior can be far from non-informative even for stationary time series models, because here it converges rapidly to a point mass at a unit root as $s$ increases. Our overall emphasis is that intuition and conventional wisdom need to be examined via critical thinking and theoretical verification before they can be trusted fully.Statistic

Isolation of single cells from human hepatoblastoma tissues for whole-exome sequencing

Summary: By combining single-cell processing with whole-exome sequencing, we have developed single-cell whole-exome sequencing to investigate the mechanisms of hepatoblastoma development and to provide potential targets and therapeutic approaches for clinical treatment. In the following protocol, we outline the steps involved in single-cell sorting, whole-genome amplification, amplification uniformity estimation, and whole-exome library construction. In addition to the cells we use, this protocol is also suitable for other cell lines and cell types.For complete details on the use and execution of this protocol, please refer to Jian et al. (2023). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics

Protocol to dissociate and isolate wide-diversity single cells by density gradient centrifugation from human hepatoblastoma tissue

Summary: Single-cell transcriptome sequencing can characterize various cell types in human liver tissue and facilitate understanding of hepatoblastoma heterogeneity. Here, we present a protocol for isolating hepatocytes and immune cells from human hepatoblastoma samples with high viability. We describe steps for tissue processing, enzymatic digestion, Percoll density gradient separation, cell lysis, cell suspension quality control, and scRNA library construction. We then detail sequencing and data analysis. This protocol is applicable to preparing single-cell suspensions from other human liver tissue samples. : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics

A high-speed, eight-wavelength visible light-infrared pyrometer for shock physics experiments

An eight-channel, high speed pyrometer for precise temperature measurement is designed and realized in this work. The addition of longer-wavelength channels sensitive at lower temperatures highly expands the measured temperature range, which covers the temperature of interest in shock physics from 1500K-10000K. The working wavelength range is 400-1700nm from visible light to near-infrared (NIR). Semiconductor detectors of Si and InGaAs are used as photoelectric devices, whose bandwidths are 50MHz and 150MHz respectively. Benefitting from the high responsivity and high speed of detectors, the time resolution of the pyrometer can be smaller than 10ns. By combining the high-transmittance beam-splitters and narrow-bandwidth filters, the peak spectrum transmissivity of each channel can be higher than 60%. The gray-body temperatures of NaI crystal under shock-loading are successfully measured by this pyrometer