The latent Markov (LM) model is a popular method for identifying distinct unobserved states and transitions between these states over time in longitudinally observed responses. The bootstrap likelihood-ratio (BLR) test yields the most rigorous test for determining the number of latent states, yet little is known about power analysis for this test. Power could be computed as the proportion of the bootstrap p values (PBP) for which the null hypothesis is rejected. This requires performing the full bootstrap procedure for a large number of samples generated from the model under the alternative hypothesis, which is computationally infeasible in most situations. This article presents a computationally feasible shortcut method for power computation for the BLR test. The shortcut method involves the following simple steps: (1) obtaining the parameters of the model under the null hypothesis, (2) constructing the empirical distributions of the likelihood ratio under the null and alternative hypotheses via Monte Carlo simulations, and (3) using these empirical distributions to compute the power. We evaluate the performance of the shortcut method by comparing it to the PBP method and, moreover, show how the shortcut method can be used for sample-size determination.
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http://dx.doi.org/10.1080/00273171.2016.1203280 | DOI Listing |
Water Res
December 2024
The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, PR China. Electronic address:
Bioremediation of Cr(Ⅵ) and ammonia is considered as a promising and cost-effective alternative to chemical and physical methods. However, Cr(Ⅵ) could inhibit nitrogen removal by inhibiting intra-/extracellular electron (IET/EET) transfer or nitrifying and denitrifying enzymes activity due to its higher solubility. In this study, we isolated a simultaneous nitrification and denitrification (SND) microorganism Acinetobacter haemolyticus RH19, capable of outcompeting oxygen to take nitrogen oxides/ammonia as electron acceptors, and studied a combined accelerant (cysteine, biotin and cytokinin) to relive the Cr(Ⅵ) stress.
View Article and Find Full Text PDFPhys Rev E
November 2024
Raymond & Beverly Sackler School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel.
We present a technique for efficiently transitioning a quantum system from an initial to a final stationary state in less time than is required by an adiabatic (quasistatic) process. Our approach makes use of Nelson's stochastic quantization, which represents the quantum system as a classical Brownian process. Thanks to this mathematical analogy, known protocols for classical overdamped systems can be translated into quantum protocols.
View Article and Find Full Text PDFEBioMedicine
December 2024
Department of Radiology, University of Calgary, Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada; Department of Clinical Neuroscience, University of Calgary, Calgary, Canada.
Background: Understanding the mechanisms of algorithmic bias is highly challenging due to the complexity and uncertainty of how various unknown sources of bias impact deep learning models trained with medical images. This study aims to bridge this knowledge gap by studying where, why, and how biases from medical images are encoded in these models.
Methods: We systematically studied layer-wise bias encoding in a convolutional neural network for disease classification using synthetic brain magnetic resonance imaging data with known disease and bias effects.
J Clin Nurs
December 2024
School of Nursing, Midwifery and Paramedicine, Australian Catholic University, Melbourne, Victoria, Australia.
Aim: To understand, from a nursing perspective, factors affecting the use of prophylactic dressings to prevent pressure injuries in acute hospitalised adults.
Background: Pressure injury causes harm to patients and incurs significant costs to health services. Significant emphasis is placed on their prevention.
J Imaging Inform Med
November 2024
Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.
The Clever Hans effect occurs when machine learning models rely on spurious correlations instead of clinically relevant features and poses significant challenges to the development of reliable artificial intelligence (AI) systems in medical imaging. This scoping review provides an overview of methods for identifying and addressing the Clever Hans effect in medical imaging AI algorithms. A total of 173 papers published between 2010 and 2024 were reviewed, and 37 articles were selected for detailed analysis, with classification into two categories: detection and mitigation approaches.
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