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Related Concept Videos

Categories and Inductive Inferences10:08

Categories and Inductive Inferences

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Source: Laboratories of Nicholaus Noles and Judith Danovitch—University of Louisville
It might be possible for the human brain to keep track of each individual person, place, or thing encountered, but that would be a very inefficient use of time and cognitive resources. Instead, humans develop categories. Categories are mental representations of real things that can be used for a variety of purposes. For example, individuals can use the perceptual features of animals to place them into a...
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HIV tropism can be inferred from the V3 region of the viral envelope. V3 is PCR amplified in triplicate using nested RT-PCR, sequenced, and interpreted using bioinformatic software. Samples with with 1 or more sequence(s) with low g2P scores are classified as non-R5...
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Cellular Impedance-Based Survival Kinetics Assay to Assess Virus Survival02:52

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This video describes a cellular impedance-based technique to study the survival kinetics of viruses that induce cytopathic effects. The method measures the electrical impedance of virus-infected adherent cells pre-treated with...
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Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions05:18

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Rotifers are microscopic zooplankton used as models in ecotoxicological and aging studies. Here we provide a protocol for powerful and reproducible measurement of survival time in Brachionus rotifers. Synchronization of culture conditions over several generations is of particular importance because maternal condition affects life history of...
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Lattice Centering and Coordination Number02:33

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The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
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Solid Plate-based Dietary Restriction in Caenorhabditis elegans06:13

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Here we present a protocol for performing solid plate-based dietary restriction in C. elegans with killed...
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Related Experiment Video

Updated: Jan 19, 2026

How Children Categorize Objects and Make Inductive Inferences
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Computationally efficient inference for center effects based on restricted mean survival time.

Xin Wang1,2, Yingchao Zhong1, Purna Mukhopadhyay3

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan.

Statistics in Medicine
|September 11, 2019
PubMed
Summary

This study introduces efficient methods for analyzing restricted mean survival time (RMST) in large datasets. The new approach simplifies computation and interpretation, making it valuable for clinical research.

Keywords:
censored datacenter effectfacility profilingfailure timerestricted mean survival time

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Area of Science:

  • Biostatistics
  • Clinical Research Methodology

Background:

  • Restricted mean survival time (RMST) is increasingly used in clinical and biostatistical studies.
  • Directly modeling RMST offers advantages in computational efficiency and covariate effect interpretation over hazard function transformations.

Purpose of the Study:

  • To propose computationally convenient methods for evaluating center effects using RMST.
  • To develop a multiplicative model for RMST that facilitates the analysis of large numbers of centers.

Main Methods:

  • A multiplicative model for RMST is assumed.
  • Estimation employs an algorithm analogous to stratification, enabling the analysis of thousands of centers.
  • Asymptotic properties of estimators are derived, and finite sample performance is assessed via simulation.

Main Results:

  • The proposed methods significantly reduce computational burden, including storage requirements and run time.
  • The methods are successfully applied to analyze data from over 5000 US dialysis facilities.
  • The approach allows for the evaluation of center effects based on RMST.

Conclusions:

  • The developed methods provide a computationally efficient and interpretable approach for analyzing RMST.
  • This technique is suitable for large-scale studies, such as evaluating numerous healthcare facilities.
  • The findings highlight the practical benefits of direct RMST modeling in biostatistical applications.