A Poisson distribution-based general model of cancer rates and a cancer risk-dependent theory of aging

This article presents a formula for modeling the lifetime incidence of cancer in humans. The formula utilizes a Poisson distribution-based "np" model to predict cancer incidence, with "n" representing the effective number of cell turnover and "p" representing the probability of single-cell transformation. The model accurately predicts the observed incidence of cancer in humans when a reduction in cell turnover due to aging is taken into account. The model also suggests that cancer development is ultimately inevitable. The article proposes a theory of aging based on this concept, called the "np" theory. According to this theory, an organism maintains its order by balancing cellular entropy through continuous proliferation. However, cellular "information entropy" in the form of accumulated DNA mutations increases irreversibly over time, restricting the total number of cells an organism can generate throughout its lifetime. When cell division slows down and fails to compensate for the increased entropy in the system, aging occurs. Essentially, aging is the phenomenon of running out of predetermined cell resources. Different species have evolved separate strategies to utilize their limited cell resources throughout their life cycle.

A Novel Iterative MLEM Image Reconstruction Algorithm Based on Beltrami Filter: Application to ECT Images

The implementation of emission-computed tomography (ECT), including positron emission tomography and single-photon emission-computed tomography, has been an important research topic in recent years and is of significant and practical importance. However, the slow rate of convergence and the computational complexity have severely impeded the efficient implementation of iterative reconstruction. By combining the maximum-likelihood expectation maximization (MLEM) iteratively along with the Beltrami filter, this paper proposes a new approach to reformulate the MLEM algorithm. Beltrami filtering is applied to an image obtained using the MLEM algorithm for each iteration. The role of Beltrami filtering is to remove mainly out-of-focus slice blurs, which are artifacts present in most existing images. To improve the quality of an image reconstructed using MLEM, the Beltrami filter employs similar structures, which in turn reduce the number of errors in the reconstructed image. Numerical image reconstruction tomography experiments have demonstrated the performance capability of the proposed algorithm in terms of an increase in signal-to-noise ratio (SNR) and the recovery of fine details that can be hidden in the data. The SNR and visual inspections of the reconstructed images are significantly improved compared to those of a standard MLEM. We conclude that the proposed algorithm provides an edge-preserving image reconstruction and substantially suppress noise and edge artifacts.

Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty

Conventional regression analysis using the least-squares method has been applied to describe bacterial behavior logarithmically. However, only the normal distribution is used as the error distribution in the least-squares method, and the variability and uncertainty related to bacterial behavior are not considered. In this paper, we propose Bayesian statistical modeling based on a generalized linear model (GLM) that considers variability and uncertainty while fitting the model to colony count data. We investigated the inactivation kinetic data of Bacillus simplex with an initial cell count of 10⁵ and the growth kinetic data of Listeria monocytogenes with an initial cell count of 10⁴. The residual of the GLM was described using a Poisson distribution for the initial cell number and inactivation process and using a negative binomial distribution for the cell number variation during growth. The model parameters could be obtained considering the uncertainty by Bayesian inference. The Bayesian GLM successfully described the results of over 50 replications of bacterial inactivation with average of initial cell numbers of 10¹, 10², and 10³ and growth with average of initial cell numbers of 10^–1, 10⁰, and 10¹. The accuracy of the developed model revealed that more than 90% of the observed cell numbers except for growth with initial cell numbers of 10¹ were within the 95% prediction interval. In addition, parameter uncertainty could be expressed as an arbitrary probability distribution. The analysis procedures can be consistently applied to the simulation process through fitting. The Bayesian inference method based on the GLM clearly explains the variability and uncertainty in bacterial population behavior, which can serve as useful information for risk assessment related to food borne pathogens.

A New Family of Continuous Probability Distributions

In this paper, a new parametric compound G family of continuous probability distributions called the Poisson generalized exponential G (PGEG) family is derived and studied. Relevant mathematical properties are derived. Some new bivariate G families using the theorems of "Farlie-Gumbel-Morgenstern copula", "the modified Farlie-Gumbel-Morgenstern copula", "the Clayton copula", and "the Renyi's entropy copula" are presented. Many special members are derived, and a special attention is devoted to the exponential and the one parameter Pareto type II model. The maximum likelihood method is used to estimate the model parameters. A graphical simulation is performed to assess the finite sample behavior of the estimators of the maximum likelihood method. Two real-life data applications are proposed to illustrate the importance of the new family.

Prospective cross-sectional study using Poisson renewal theory to study phase singularity formation and destruction rates in atrial fibrillation (RENEWAL-AF): Study design

BACKGROUND

Unstable functional reentrant circuits known as rotors have been consistently observed in atrial fibrillation and are mechanistically believed critical to the maintenance of the arrhythmia. Recently, using a Poisson renewal theory-based quantitative framework, we have demonstrated that rotor formation (λf) and destruction rates (λd) can be measured using in vivo electrophysiologic data. However, the association of λf and λd with clinical, electrical, and structural markers of atrial fibrillation phenotype is unknown.

METHODS

RENEWAL-AF is a multicenter prospective cross-sectional study recruiting adult patients with paroxysmal or persistent atrial fibrillation undergoing clinically indicated catheter ablation. Patients will undergo intraprocedural electrophysiologic atrial fibrillation mapping, with λf and λd to be determined from 2-minute unipolar electrogram recordings acquired before ablation. The primary objective will be to determine the association of λf and λd as markers of fibrillatory dynamics with clinical, electrical, and structural markers of atrial fibrillation clinical phenotype, measured by preablation transthoracic echocardiogram and cardiac magnetic resonance imaging. An exploratory objective is the noninvasive assessment of λf and λd using surface ECG characteristics via a machine learning approach.

RESULTS

Not applicable.

CONCLUSION

This pilot study will provide insight into the correlation between λf/λd with clinical, electrophysiological, and structural markers of atrial fibrillation phenotype and provide a foundation for the development of noninvasive assessment of λf/λd using surface ECG characteristics will help expand the use of λf/λd in clinical practice.

Optimizing Count Responses in Surveys: A Machine-learning Approach

Count responses with grouping and right censoring have long been used in surveys to study a variety of behaviors, status, and attitudes. Yet grouping or right-censoring decisions of count responses still rely on arbitrary choices made by researchers. We develop a new method for evaluating grouping and right-censoring decisions of count responses from a (semisupervised) machine-learning perspective. This article uses Poisson multinomial mixture models to conceptualize the data-generating process of count responses with grouping and right censoring and demonstrates the link between grouping-scheme choices and asymptotic distributions of the Poisson mixture. To search for the optimal grouping scheme maximizing objective functions of the Fisher information (matrix), an innovative three-step M algorithm is then proposed to process infinitely many grouping schemes based on Bayesian A-, D-, and E-optimalities. A new R package is developed to implement this algorithm and evaluate grouping schemes of count responses. Results show that an optimal grouping scheme not only leads to a more efficient sampling design but also outperforms a nonoptimal one even if the latter has more groups.

[Progress in digital PCR technology and application]

Digital PCR is an emerging analysis technology for absolute quantification after realtime-PCR. Through digital PCR, single DNA molecules are distributed into isolated reactions, and the product with fluorescence signal can be detected and analyzed after amplification. With the advantages of higher sensitivity and accuracy, digital PCR, independent of a standard curve, is developing rapidly and applied widely to the next generation sequencing and detection fields, such as gene mutation, copy number variation, microorganism, and genetically modified food. In this article, we reviewed the quantitative method and research progress of digital PCR technology in the main application fields.

Unobtrusive Detection of Mild Cognitive Impairment in Older Adults Through Home Monitoring

The early detection of dementias such as Alzheimer's disease can in some cases reverse, stop, or slow cognitive decline and in general greatly reduce the burden of care. This is of increasing significance as demographic studies are warning of an aging population in North America and worldwide. Various smart homes and systems have been developed to detect cognitive decline through continuous monitoring of high risk individuals. However, the majority of these smart homes and systems use a number of predefined heuristics to detect changes in cognition, which has been demonstrated to focus on the idiosyncratic nuances of the individual subjects, and thus, does not generalize. In this paper, we address this problem by building generalized linear models of home activity of older adults monitored using unobtrusive sensing technologies. We use inhomogenous Poisson processes to model the presence of the recruited older adults within different rooms throughout the day. We employ an information theoretic approach to compare the generalized linear models learned, and we observe significant statistical differences between the cognitively intact and impaired older adults. Using a simple thresholding approach, we were able to detect mild cognitive impairment in older adults with an average area under the ROC curve of 0.716 and an average area under the precision-recall curve of 0.706 using activity models estimated over a time window of 12 weeks.

Poisson-event-based analysis of cell proliferation

A protocol for the assessment of cell proliferation dynamics is presented. This is based on the measurement of cell division events and their subsequent analysis using Poisson probability statistics. Detailed analysis of proliferation dynamics in heterogeneous populations requires single cell resolution within a time series analysis and so is technically demanding to implement. Here, we show that by focusing on the events during which cells undergo division rather than directly on the cells themselves a simplified image acquisition and analysis protocol can be followed, which maintains single cell resolution and reports on the key metrics of cell proliferation. The technique is demonstrated using a microscope with 1.3 μm spatial resolution to track mitotic events within A549 and BEAS‐2B cell lines, over a period of up to 48 h. Automated image processing of the bright field images using standard algorithms within the ImageJ software toolkit yielded 87% accurate recording of the manually identified, temporal, and spatial positions of the mitotic event series. Analysis of the statistics of the interevent times (i.e., times between observed mitoses in a field of view) showed that cell division conformed to a nonhomogeneous Poisson process in which the rate of occurrence of mitotic events, λ exponentially increased over time and provided values of the mean inter mitotic time of 21.1 ± 1.2 hours for the A549 cells and 25.0 ± 1.1 h for the BEAS‐2B cells. Comparison of the mitotic event series for the BEAS‐2B cell line to that predicted by random Poisson statistics indicated that temporal synchronisation of the cell division process was occurring within 70% of the population and that this could be increased to 85% through serum starvation of the cell culture. © 2015 The Authors. Published by Wiley Periodicals, Inc.

Calibration-free absolute quantification of particle concentration by statistical analyses of photoacoustic signals in vivo

Currently, laser fluence calibration is typically required for quantitative measurement of particle concentration in photoacoustic imaging. Here, we present a calibration-free method to quantify the absolute particle concentration by statistically analyzing photoacoustic signals. The proposed method is based on the fact that Brownian motion induces particle count fluctuation in the detection volume. If the count of particles in the detection volume is assumed to follow the Poisson distribution, its expected value can be calculated by the photoacoustic signal mean and variance. We first derived a theoretical model for photoacoustic signals. Then, we applied our method to quantitative measurement of different concentrations of various particles, including red blood cells. Finally, we performed in vivo experiments to demonstrate the potential of our method in biological applications. The experimental results agreed well with the predictions from the theoretical model suggesting that our method can be used for noninvasive measurement of absolute particle concentrations in deep tissue without fluence calibration.

Sample size calculation for differential expression analysis of RNA-seq data under Poisson distribution

Sample size determination is an important issue in the experimental design of biomedical research. Because of the complexity of RNA-seq experiments, however, the field currently lacks a sample size method widely applicable to differential expression studies utilising RNA-seq technology. In this report, we propose several methods for sample size calculation for single-gene differential expression analysis of RNA-seq data under Poisson distribution. These methods are then extended to multiple genes, with consideration for addressing the multiple testing problem by controlling false discovery rate. Moreover, most of the proposed methods allow for closed-form sample size formulas with specification of the desired minimum fold change and minimum average read count, and thus are not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size formulas are presented; the results indicate that our methods work well, with achievement of desired power. Finally, our sample size calculation methods are applied to three real RNA-seq data sets.

Confidence intervals and statistical testing for ratio measures of percent change

In public health and medical research, ratio measures of percent change relative to baseline are often used to express a change in disease incidence. Estimating variance becomes more complex when the comparison is to an expectation based on previous data (E), rather than to an observed value (O). In 2009, the decline in reported tuberculosis (TB) cases was the largest single-year decrease since national TB surveillance began in 1953. To investigate the 2009 TB decline compared with expected counts, we analyzed TB cases reported to the Center for Disease Control and Prevention's National Tuberculosis Surveillance System. We log-transformed case counts for 2000-2008, and performed linear regression stratified by patient and clinical characteristics. We calculated relative declines from expectation as (O - E) ∕ E for patient subgroups, and constructed 95% confidence intervals for TB declines. We then formulated a Z-score test statistic comparing declines across patient subgroups under the null hypothesis that the difference of the two ratio measures was zero. We illustrate our methods by comparing 2009 declines from expectation for US-born versus foreign-born patients. Predicted values and confidence intervals assessed the magnitude of unexpected TB declines within patient groups, while statistical tests comparing ratio measures evaluated relative TB declines across groups. Published 2012. This article is a US Government work and is in the public domain in the USA.

A novel experience in the use of control charts for the detection of nosocomial infection outbreaks

OBJECTIVE

This study aims to compare different control charts to monitor the nosocomial infection rate per 1,000 patient-days.

METHODS

The control charts considered in this study were the traditional Shewhart chart and a variation of this, the Cumulative Sum and Exponentially Weighted Moving Average charts.

RESULTS

We evaluated 238 nosocomial infections that were registered in the intensive care unit and were detected by the Committee for Nosocomial Infection Control in a university hospital in Belo Horizonte, Brazil, in 2004 and 2005. The results showed that the traditional Shewhart chart was the most appropriate method for monitoring periods with large deviations, while the Exponentially Weighted Moving Average and Cumulative Sum charts were better for monitoring periods with smaller deviations of the mean infection rate.

CONCLUSION

The ability to detect nosocomial outbreaks was improved by using the information provided by all three different control charts.

Excess mortality related to the August 2003 heat wave in France

OBJECTIVES

From August 1st to 20th, 2003, the mean maximum temperature in France exceeded the seasonal norm by 11-12 degrees C on nine consecutive days. A major increase in mortality was then observed, which main epidemiological features are described herein.

METHODS

The number of deaths observed from August to November 2003 in France was compared to those expected on the basis of the mortality rates observed from 2000 to 2002 and the 2003 population estimates.

RESULTS

From August 1st to 20th, 2003, 15,000 excess deaths were observed. From 35 years age, the excess mortality was marked and increased with age. It was 15% higher in women than in men of comparable age as of age 45 years. Excess mortality at home and in retirement institutions was greater than that in hospitals. The mortality of widowed, single and divorced subjects was greater than that of married people. Deaths directly related to heat, heatstroke, hyperthermia and dehydration increased massively. Cardiovascular diseases, ill-defined morbid disorders, respiratory diseases and nervous system diseases also markedly contributed to the excess mortality. The geographic variations in mortality showed a clear age-dependent relationship with the number of very hot days. No harvesting effect was observed.

CONCLUSIONS

Heat waves must be considered as a threat to European populations living in climates that are currently temperate. While the elderly and people living alone are particularly vulnerable to heat waves, no segment of the population may be considered protected from the risks associated with heat waves.

A comparative study of a theoretical neural net model with MEG data from epileptic patients and normal individuals

OBJECTIVE

The aim of this study was to compare a theoretical neural net model with MEG data from epileptic patients and normal individuals.

METHODS

Our experimental study population included 10 epilepsy sufferers and 10 healthy subjects. The recordings were obtained with a one-channel biomagnetometer SQUID in a magnetically shielded room.

RESULTS

Using the method of x2-fitting it was found that the MEG amplitudes in epileptic patients and normal subjects had Poisson and Gauss distributions respectively. The Poisson connectivity derived from the theoretical neural model represents the state of epilepsy, whereas the Gauss connectivity represents normal behavior. The MEG data obtained from epileptic areas had higher amplitudes than the MEG from normal regions and were comparable with the theoretical magnetic fields from Poisson and Gauss distributions. Furthermore, the magnetic field derived from the theoretical model had amplitudes in the same order as the recorded MEG from the 20 participants.

CONCLUSION

The approximation of the theoretical neural net model with real MEG data provides information about the structure of the brain function in epileptic and normal states encouraging further studies to be conducted.

Trends in breast cancer incidence in Hong Kong between 1973 and 1999: an age-period-cohort analysis

Hong Kong has the highest breast cancer incidence in Asia and studying secular changes in its rates may lead to hypotheses regarding disease aetiology and also predictions of future trends for China. We examined statistics from the Hong Kong Cancer Registry based on 26 566 cases of invasive breast cancer from 1973 to 1999. The trends in breast cancer incidence were studied using log-linear longitudinal models. We further analysed the independent effects of chronological age, time period and birth cohort on incidence trends using age-period-cohort modelling. The average annual per cent change of the age-standardised incidence was 3.6% during 1973-1999. Age-period-cohort modelling indicated the incidence development was predominantly a cohort effect, where the rise in relative risk was seemingly linear in successive birth cohorts, showing a 2-3-fold difference when comparing women born in the 1960's with those born around 1900. Our results suggest that direct and indirect consequences of westernisation may have been responsible for most of the observed increase in breast cancer incidence. As China moves towards a more westernised way of life, we can expect an emerging epidemic of breast cancer as Hong Kong's experience has demonstrated.

Mode of action of a supernatant activity from T-cell cultures that nonspecifically stimulates the humoral immune response

The mode of action of "allogeneic supernatant" (the culture supernatant of a 24-hr mixedlymphocyte reaction), has been studied. This factor stimulates the response of spleen cell cultures depleted in thymus-derived lymphoid cells (T-cells) to antigens that elicit a thymus-dependent response. We used a limiting dilution analysis, in which the frequency and size of response of individual bone-marrow-derived lymphoid cells (B-cells) could be measured. In confirmation of other reports, the occurrence of B-cells responding to antigen under different conditions was shown to follow a Poisson distribution in mouse spleen cell suspensions. Allogeneic supernatant increased responses to thymus-dependent antigens, both by increasing the frequency of B-cells whose response is initiated and by increasing the numbers of antibody-forming cells obtained from each responding B-cell. Two fractions were obtained by dialysis of the supernatant. The nondialyzable fraction contained factors able to increase both the frequency of B-cells responding to sheep erythrocytes, and the size of the responding unit. The dialysate contained factors that were only able to increase the numbers of antibody-forming cells obtained per responding B-cell from B-cells whose response had already been initiated by antigen-specific T-cells. Since the nondialyzable factors were active in the absence of detectable functional T-cells, it was concluded that these factors, produced by T-cells, might represent one mechanism whereby T-cells cooperate with B-cells in the initiation or development of a humoral immune response.