A Heteroscedastic Bayesian Generalized Logistic Regression Model with Application to Scaling Problems

Power law scaling models have been used to understand the complexity of systems as diverse as cities, neurological activity, and rainfall and lightning. In the scaling framework, power laws and standard linear regression methods are widely used to estimate model parameters with assumed normality and fixed variance. Generalized linear models (GLM) can accommodate a wider range of distributions where the chosen distribution must meet the assumptions of the data to prevent model bias. We present a widely applicable Bayesian generalized logistic regression (BGLR) framework to more flexibly model a continuous real response addressing skew and heteroscedasticity. The Generalized Logistic Distribution (GLD) was selected to flexibly model skewed continuous data. This resulted in a nonlinear posterior distribution which may not have an analytical solution which can be solved numerically with Markov Chain Monte Carlo (MCMC) methods. We compared the BGLR model to standard and Bayesian normal models having fixed and varying variance when fitting power laws to 759 days of COVID-19 data. The BGLR yielded information beyond existing methods about the evolution of skew and skedasticity while revealing parameter bias of widely used methods. The BGLR flexibly modelled the complex characteristics necessary for an improved understanding of the propagation and dynamics of this infectious disease. The model is generally applicable and can be used as a template for modeling complexity with other distributions

Bivariate genetic modelling of the response to an oral glucose tolerance challenge: A gene x environment interaction approach

AIMS/HYPOTHESIS: Twin and family studies have shown the importance of genetic factors influencing fasting and 2 h glucose and insulin levels. However, the genetics of the physiological response to a glucose load has not been thoroughly investigated. METHODS: We studied 580 monozygotic and 1,937 dizygotic British female twins from the Twins UK Registry. The effects of genetic and environmental factors on fasting and 2 h glucose and insulin levels were estimated using univariate genetic modelling. Bivariate model fitting was used to investigate the glucose and insulin responses to a glucose load, i.e. an OGTT. RESULTS: The genetic effect on fasting and 2 h glucose and insulin levels ranged between 40% and 56% after adjustment for age and BMI. Exposure to a glucose load resulted in the emergence of novel genetic effects on 2 h glucose independent of the fasting level, accounting for about 55% of its heritability. For 2 h insulin, the effect of the same genes that already influenced fasting insulin was amplified by about 30%. CONCLUSIONS/INTERPRETATION: Exposure to a glucose challenge uncovers new genetic variance for glucose and amplifies the effects of genes that already influence the fasting insulin level. Finding the genes acting on 2 h glucose independently of fasting glucose may offer new aetiological insight into the risk of cardiovascular events and death from all causes

Development of a Multiphoton Fluorescence Lifetime Imaging Microscopy (FLIM) system using a Streak Camera

We report the development and detailed calibration of a multiphoton fluorescence lifetime imaging system (FLIM) using a streak camera. The present system is versatile with high spatial (0.2 micron) and temporal (50 psec) resolution and allows rapid data acquisition and reliable and reproducible lifetime determinations. The system was calibrated with standard fluorescent dyes and the lifetime values obtained were in very good agreement with values reported in literature for these dyes. We also demonstrate the applicability of the system to FLIM studies in cellular specimens including stained pollen grains and fibroblast cells expressing green fluorescent protein. The lifetime values obtained matched well with those reported earlier by other groups for these same specimens. Potential applications of the present system include the measurement of intracellular physiology and Fluorescence Resonance Energy Transfer (FRET) imaging which are discussed in the context of live cell imaging

A computational model of excitation and contraction in uterine myocytes from the pregnant rat

Aberrant uterine myometrial activities in humans are major health issues. However, the cellular and tissue mechanism(s) that maintain the uterine myometrium at rest during gestation, and that initiate and maintain long-lasting uterine contractions during delivery are incompletely understood. In this study we construct a computational model for describing the electrical activity (simple and complex action potentials), intracellular calcium dynamics and mechanical contractions of isolated uterine myocytes from the pregnant rat. The model reproduces variant types of action potentials – from spikes with a smooth plateau, to spikes with an oscillatory plateau, to bursts of spikes – that are seen during late gestation under different physiological conditions. The effects of the hormones oestradiol (via reductions in calcium and potassium selective channel conductance), oxytocin (via an increase in intracellular calcium release) and the tocolytic nifedipine (via a block of L-type calcium channels currents) on action potentials and contractions are also reproduced, which quantitatively match to experimental data. All of these results validated the cell model development. In conclusion, the developed model provides a computational platform for further investigations of the ionic mechanism underlying the genesis and control of electrical and mechanical activities in the rat uterine myocytes

Link prediction in complex networks: a local na\"{\i}ve Bayes model

Common-neighbor-based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In such method, each common neighbor of two nodes contributes equally to the connection likelihood. In this Letter, we argue that different common neighbors may play different roles and thus lead to different contributions, and propose a local na\"{\i}ve Bayes model accordingly. Extensive experiments were carried out on eight real networks. Compared with the common-neighbor-based methods, the present method can provide more accurate predictions. Finally, we gave a detailed case study on the US air transportation network.Comment: 6 pages, 2 figures, 2 table

Primary hyperparathyroidism: review and recommendations on evaluation, diagnosis, and management. A Canadian and international consensus

The purpose of this review is to assess the most recent evidence in the management of primary hyperparathyroidism (PHPT) and provide updated recommendations for its evaluation, diagnosis and treatment. A Medline search of "Hyperparathyroidism. Primary" was conducted and the literature with the highest levels of evidence were reviewed and used to formulate recommendations. PHPT is a common endocrine disorder usually discovered by routine biochemical screening. PHPT is defined as hypercalcemia with increased or inappropriately normal plasma parathyroid hormone (PTH). It is most commonly seen after the age of 50 years, with women predominating by three to fourfold. In countries with routine multichannel screening, PHPT is identified earlier and may be asymptomatic. Where biochemical testing is not routine, PHPT is more likely to present with skeletal complications, or nephrolithiasis. Parathyroidectomy (PTx) is indicated for those with symptomatic disease. For asymptomatic patients, recent guidelines have recommended criteria for surgery, however PTx can also be considered in those who do not meet criteria, and prefer surgery. Non-surgical therapies are available when surgery is not appropriate. This review presents the current state of the art in the diagnosis and management of PHPT and updates the Canadian Position paper on PHPT. An overview of the impact of PHPT on the skeleton and other target organs is presented with international consensus. Differences in the international presentation of this condition are also summarized

Cotton Leaf Curl Multan Virus C4 Protein Suppresses Both Transcriptional and Post-transcriptional Gene Silencing by Interacting with SAM Synthetase

Author summary Geminiviruses are single-stranded DNA (ssDNA) viruses that infect a wide range of plant species and are responsible for substantial crop damage worldwide. However, how geminiviruses inhibit plant antiviral gene silencing defense is unclear. Here, we report that a single geminiviral protein CLCuMuV C4 inhibits both plant transcriptional gene silencing (TGS) and post-transcriptional gene silencing (PTGS) to promote an effective viral infection. We show that CLCuMuV C4 protein interacts with SAMS, a core enzyme in methyl cycle, and inhibits SAMS activity. Overexpression of CLCuMuV C4 reduces the DNA methylation levels of both a transgene and an endogenous locus. Further, silencing of SAMS reduced both TGS and PTGS, and enhanced viral infection while CLCuMuV virus carrying a mutation in C4 that fails to interact with SAMS showed decreased infection. These findings reveal a novel mechanism by which the CLCuMuV C4 protein suppress SAMS mediated TGS and PTGS, leading to enhanced viral infection in plant