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Abstract Full quantification of Positron Emission Tomography (PET) requires an arterial input function (AIF) for measurement of certain targets, or using particular radiotracers, or for the quantification of specific outcome measures. The AIF represents the measurement of radiotracer concentrations in the arterial blood plasma over the course of the PET examination. Measurement of the AIF is prone to error as it is a composite measure created from the combination of multiple measurements of different samples with different equipment, each of which can be sources of measurement error. Moreover, its measurement requires a high degree of temporal granularity for early time points, which necessitates a compromise between quality and quantity of recorded samples. For these reasons, it is often desirable to fit models to this data in order to improve its quality before using it for quantification of radiotracer binding in the tissue. The raw observations of radioactivity in arterial blood and plasma samples are derived from radioactive decay, which is measured as a number of recorded counts. Count data have several specific properties, including the fact that they cannot be negative as well as a particular mean-variance relationship. Poisson regression is the most principled modelling strategy for working with count data, as it both incorporates and exploits these properties. However, no previous studies to our knowledge have taken this approach, despite the advantages of greater efficiency and accuracy which result from using the appropriate distributional assumptions. Here, we implement a Poisson regression modelling approach for the AIF as proof-of-concept of its application. We applied both parametric and non-parametric models for the input function curve. We show that a negative binomial distribution is a more appropriate error distribution for handling overdispersion. Furthermore, we extend this approach to a hierarchical non-parametric model which is shown to be highly resilient to missing data. We thus demonstrate that Poisson regression is both feasible and effective when applied to AIF data, and propose that this is a promising strategy for modelling blood count data for PET in future.

The KCNE2 protein is encoded by the kcne2 gene and is a member of the KCNE protein family, also known as the MinK-related protein 1 (MiRP1). It is mostly present in the epicardium of the heart and gastric mucosa, and it is also found in the thyroid, pancreatic islets, liver and lung, among other locations, to a lesser extent. It is involved in numerous physiological processes because of its ubiquitous expression and partnering promiscuity, including the modulation of voltage-dependent potassium and calcium channels involved in cardiac action potential repolarization, and regulation of secretory processes in multiple epithelia, such as gastric acid secretion, thyroid hormone synthesis, generation and secretion of cerebrospinal fluid. Mutations in the KCNE2 gene or aberrant expression of the protein may play a critical role in cardiovascular, neurological, metabolic and multisystem disorders. This article provides an overview of the advancements made in understanding the physiological functions in organismal homeostasis and the pathophysiological consequences of KCNE2 in multisystem diseases.

A strong inclination toward an important or preferred activity is a critical factor that drives individual to engage in corresponding activities. This study focuses on how entrepreneurs, influenced by obsessive passion, are motivated to put great effort into taking advantage of business opportunities and accomplishing entrepreneurial goals. By using SPSS and AMOS tools to analyze the multi-source questionnaire of 208 entrepreneurs, the research results show that obsessive passion can promote entrepreneurial performance and opportunity recognition plays a mediating role. In addition, endogenous and exogenous fear of failure play different moderating roles in the effect of obsessive passion on opportunity recognition. The research conclusion deepens the theoretical understanding of entrepreneurial passion, opportunity recognition, and fear of failure at a more subtle level.

The U(Ⅵ) biomineralization on microorganisms will affect the chemical form and migration behavior of uranium in the environment. However, there are few studies on the kinetic behavior and the relevant mechanism of U(Ⅵ) biomineralization in the growth of microorganisms. In this work, the effects of pH values, sodium glycerophosphate (SGP) and U(Ⅵ) concentration on the biomineralization behavior of U(Ⅵ) in the growth of Bacillus sp.dwc-2, a bacterial strain isolated from a potential (ultra-) low radioactive waste disposal site, and the possible mechanism were investigated. The results indicate that the formation of sodium uranyl phosphate (NaUO2PO4·3H2O) induced by Bacillus sp.dwc-2 as an enzyme-catalyzed reaction in the presence of factors such as pH value, SGP concentration, and initial U(Ⅵ) concentration. The increase of U(Ⅵ) concentration (50-150 mg/L) stimulates the expression of phosphatase activity and phosphate metabolism at the initial stage of biomineralization (0-12 hours), leading to a shift of biomineralization induced by Bacillus sp.dwc-2 at the 12th hour. The increase of the SGP concentration could promote the catalytic reaction rate of phosphatase, and then promote the U(Ⅵ) biomineralization. When the SGP concentration increases from 25 mmol/L to 125 mmol/L, the time of biomineralization induced by Bacillus sp.dwc-2 advances from 24 hours to 12 hours. The phosphatase activity increases rapidly at the initial stage of biomineralization (0-12 hours) under neutral and alkaline conditions, thus promoting the consumption of phosphate and the U(Ⅵ) biomineralization. Especially under alkaline condition, the sediment is transformed into obvious crystal minerals at the 8th hour. The above research results are helpful to better understand the behavior and mechanism of uranium biomineralization, and also have reference value for bioremediation of uranium contaminated environment.