Recently, Schmid and Spekkens studied the quantum contextuality in terms of state discrimination. By dealing with the minimum error discrimination of two quantum states with identical prior probabilities, they reported that quantum contextual advantage exists. Meanwhile, if one notes a striking observation that the selection of prior probability can affect the quantum properties of the system, it is necessary to verify whether the quantum contextual advantage depends on the prior probabilities of the given states. In this paper, we consider the minimum error discrimination of two states with arbitrary prior probabilities, in which both states are pure or mixed. We show that the quantum contextual advantage in state discrimination may depend on the prior probabilities of the given states. In particular, even though the quantum contextual advantage always exists in the state discrimination of two nonorthogonal pure states with nonzero prior probabilities, the quantum contextual advantage depends on prior probabilities in the state discrimination of two mixed states.
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http://dx.doi.org/10.3390/e23121583 | DOI Listing |
J Phycol
January 2025
Bamfield Marine Sciences Centre, Bamfield, British Columbia, Canada.
Kelp forests are among the most abundant and productive marine ecosystems but are under threat from climate change and other anthropogenic stressors. Although knowledge is growing about how the abundance and distribution of kelp forests are changing, much less is known about the "non-lethal" effects that global change is having on the performance and health of kelp populations in areas where they persist. Here we assessed the age distribution of two common stipitate kelp species, Laminaria setchelli and Pterygophora californica, at Wizard Islet in Barkley Sound, British Columbia, Canada, and compared these data to historical demographic data collected by De Wreede (1984) and Klinger and DeWreede (1988) from the same site between 1981 and 1983.
View Article and Find Full Text PDFStat Med
February 2025
Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas.
Advances in next-generation sequencing technology have enabled the high-throughput profiling of metagenomes and accelerated microbiome studies. Recently, there has been a rise in quantitative studies that aim to decipher the microbiome co-occurrence network and its underlying community structure based on metagenomic sequence data. Uncovering the complex microbiome community structure is essential to understanding the role of the microbiome in disease progression and susceptibility.
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February 2025
Departamento de Estadística, Pontificia Universidad Católica de Chile, Santiago, Chile.
More than half of the world's population is exposed to mosquito-borne diseases, leading to millions of cases and hundreds of thousands of deaths every year. Analyzing this type of data is complex and poses several interesting challenges, mainly due to the usually vast geographic area involved, the peculiar temporal behavior, and the potential correlation between infections. Motivation for this work stems from the analysis of tropical disease data, namely, the number of cases of dengue and chikungunya, for the 145 microregions in Southeast Brazil from 2018 to 2022.
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January 2025
Université Paris-Saclay, INRAE, UMR PNCA, Palaiseau, AgroParisTech, 91120, France.
The Global Burden of Diseases (GBD) network has proposed theoretical minimum risk exposure level (TMREL) for leading risk factors associated with diet that minimize the risk of morbimortality from chronic diseases. TMREL can be applied to develop follow-up or evaluation indicators in individual studies. The validity of these scores can be tested by assessing associations with health outcomes in prospective cohorts.
View Article and Find Full Text PDFBioengineering (Basel)
January 2025
SP Jain Global School of Management, Academic City, Dubai P.O. Box 502345, United Arab Emirates.
Diabetes causes an increase in the level of blood sugar, which leads to damage to various parts of the human body. Diabetes data are used not only for providing a deeper understanding of the treatment mechanisms but also for predicting the probability that one might become sick. This paper proposes a novel methodology to perform classification in the case of heavy class imbalance, as observed in the PIMA diabetes dataset.
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