A life in light - in honor of David Mauzerall on his 95th birthday.

David Mauzerall was born on July 22, 1929 to a working-class family in the small, inland textile town of Sanford, Maine. Those humble origins instilled a lifelong frugality and an innovative spirit. After earning his PhD degree in 1954 in physical organic chemistry with Frank Westheimer at the University of Chicago, he joined The Rockefeller Institute for Medical Research (now University) as a postdoctoral fellow that summer, rose to the rank of professor, and remained there for the rest of his career. His work over more than 60 years encompassed porphyrin biosynthesis, photoinduced electron-transfer reactions in diverse architectures (solutions, bilayer lipid membranes, reaction centers, chromatophores, and intact leaves), the light-saturation curve of photosynthesis, statistical treatments of photoreactions, and "all-things porphyrins." His research culminated in studies he poetically referred to as "listening to leaves" through the use of pulsed photoacoustic spectroscopy to probe the course and thermodynamics of photosynthesis in its native state. His research group was always small; indeed, of 185 total publications, 39 were singly authored. In brief, David Mauzerall has blended a deep knowledge of distinct disciplines of physical organic chemistry, photochemistry, spectroscopy and biophysics with ingenious experimental methods, incisive mathematical analysis, pristine personal integrity, and unyielding love of science to deepen our understanding of photosynthesis in its broadest context. He thought creatively - and always independently. His work helped systematize the fields of photosynthesis and the origin of life and made them more quantitative. The present article highlights a number of salient scientific discoveries and includes comments from members of his family, friends, and collaborators (Gary Brudvig, Greg Edens, Paul Falkowski, Alzatta Fogg, G. Govindjee, Nancy Greenbaum, Marilyn Gunner, Harvey Hou, Denise and Michele Mauzerall, Thomas Moore, and William Parson) as part of a celebration of his 95th birthday.

The Miller-Ross Poisson distribution and its applications to certain classes of bi-univalent functions related to Horadam polynomials.

Within the scope of this research, we introduce a novel category of bi-univalent functions. Horadam polynomials are utilized to characterize these functions by utilizing series from the Poisson distribution of the Miller-Ross type. Functions from these new categories have been used to construct estimates for the Fekete-Szego functional, as well as estimates of the Taylor-Maclaurin coefficients |l2| and |l3|. These projections were created for the methods in each of these brand-new subclasses. We made some additional discoveries after, focusing on the traits that contributed to our initial findings.

Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.

PURPOSE: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges as a consequence of its labor-intensive nature and dependency on expert knowledge. Existing automated technologies face limitations in accurately identifying dicentric chromosomes (DCs), resulting in decreased precision for radiation dose estimation. Furthermore, in the process of identifying DCs through automatic or semi-automatic methods, the resulting distribution could demonstrate under-dispersion or over-dispersion, which results in significant deviations from the Poisson distribution. In response to these issues, we developed an algorithm that employs deep learning to automatically identify chromosomes and perform fully automatic and accurate estimation of diverse radiation doses, adhering to a Poisson distribution. MATERIALS AND METHODS: The dataset utilized for the dose estimation algorithm was generated from 30 healthy donors, with samples created across seven doses, ranging from 0 to 4 Gy. The procedure encompasses several steps: extracting images for dose estimation, counting chromosomes, and detecting DC and fragments. To accomplish these tasks, we utilize a diverse array of artificial neural networks (ANNs). The identification of DCs was accomplished using a detection mechanism that integrates both deep learning-based object detection and classification methods. Based on these detection results, dose-response curves were constructed. A dose estimation was carried out by combining a regression-based ANN with the Monte-Carlo method. RESULTS: In the process of extracting images for dose analysis and identifying DCs, an under-dispersion tendency was observed. To rectify the discrepancy, classification ANN was employed to identify the results of DC detection. This approach led to satisfaction of Poisson distribution criteria by 32 out of the initial pool of 35 data points. In the subsequent stage, dose-response curves were constructed using data from 25 donors. Data provided by the remaining five donors served in performing dose estimations, which were subsequently calibrated by incorporating a regression-based ANN. Of the 23 points, 22 fell within their respective confidence intervals at p < .05 (95%), except for those associated with doses at levels below 0.5 Gy, where accurate calculation was obstructed by numerical issues. The accuracy of dose estimation has been improved for all radiation levels, with the exception of 1 Gy. CONCLUSIONS: This study successfully demonstrates a high-precision dose estimation method across a general range up to 4 Gy through fully automated detection of DCs, adhering strictly to Poisson distribution. Incorporating multiple ANNs confirms the ability to perform fully automated radiation dose estimation. This approach is particularly advantageous in scenarios such as large-scale radiological incidents, improving operational efficiency and speeding up procedures while maintaining consistency in assessments. Moreover, it reduces potential human error and enhances the reliability of results.

N-mixture models for population estimation: Application in spotted lanternfly egg mass survey.

Population density and structure are critical to nature conservation and pest management. Traditional sampling methods such as capture-mark-recapture and catch-effort can't be used in situations where catching, marking, or removing individuals are not feasible. N-mixture models use repeated count data to estimate population abundance based on detection probability. They are widely adopted in wildlife surveys in recent years to account for imperfect detection. However, its application in entomology is relatively new. In this paper, we describe the general procedures of N-mixture models in population studies from data collection to model fitting and evaluation. Using Lycorma delicatula egg mass survey data at 28 plots in seven sites from the field, we found that detection probability (p) was negatively correlated with tree diameter at breast height (DBH), ranged from 0.516 [95 % CI: 0.470-0.561] to 0.614 [95 % CI: 0.566-0.660] between the 1st and the 3rd sample period. Furthermore, egg mass abundance (λ) was positively associated with basal area (BA) for the sample unit (single tree), with more egg masses on tree of heaven (TOH) trees. More egg masses were also expected on trees of other species in TOH plots. Predicted egg mass density (masses/100 m2) ranged from 5.0 (95 % CI: 3.0-16.0) (Gordon) to 276.9 (95 % CI: 255.0-303.0) (Susquehannock) for TOH plots, and 11.0 (95 % CI: 9.00-15.33) (Gordon) to 228.3 (95 % CI: 209.7-248.3) (Burlington) for nonTOH plots. Site-specific abundance estimates from N-mixture models were generally higher compared to observed maximum counts. N-mixture models could have great potential in insect population surveys in agriculture and forestry in the future.

eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.

BACKGROUND: Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. RESULTS: We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression. CONCLUSIONS: eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

How much multiple paternity should we expect? A study of birds and contrast with mammals.

Parentage analyses via molecular markers have revealed multiple paternity within the broods of polytocous species, reshaping our understanding of animal behavior, ecology, and evolution. In a meta-analysis of multiple paternity in bird and mammal species, we conducted a literature search and found 138 bird and 64 mammal populations with microsatellite DNA paternity results. Bird populations averaged 19.5% multiple paternity and mammals more than twice that level (46.1%). We used a Bayesian approach to construct a null model for how multiple paternity should behave at random among species, under the assumption that all mated males have equal likelihood of siring success, given mean brood size and mean number of sires. We compared the differences between the null model and the actual probabilities of multiple paternity. While a few bird populations fell close to the null model, most did not, averaging 34.0-percentage points below null model predictions; mammals had an average probability of multiple paternity 13.6-percentage points below the null model. Differences between bird and mammal species were also subjected to comparative phylogenetic analyses that generally confirmed our analyses that did not adjust for estimated historical relationships. Birds exhibited extremely low probabilities of multiple paternity, not only compared to mammals but also relative to other major animal taxa. The generally low probability of multiple paternity in birds might be produced by a variety of factors, including behaviors that reflect sexual selection (extreme mate guarding or unifocal female choice) and sperm competition (e.g., precedence effects favoring fertilization by early or late matings).

Understanding Ability and Reliability Differences Measured with Count Items: The Distributional Regression Test Model and the Count Latent Regression Model.

In psychology and education, tests (e.g., reading tests) and self-reports (e.g., clinical questionnaires) generate counts, but corresponding Item Response Theory (IRT) methods are underdeveloped compared to binary data. Recent advances include the Two-Parameter Conway-Maxwell-Poisson model (2PCMPM), generalizing Rasch's Poisson Counts Model, with item-specific difficulty, discrimination, and dispersion parameters. Explaining differences in model parameters informs item construction and selection but has received little attention. We introduce two 2PCMPM-based explanatory count IRT models: The Distributional Regression Test Model for item covariates, and the Count Latent Regression Model for (categorical) person covariates. Estimation methods are provided and satisfactory statistical properties are observed in simulations. Two examples illustrate how the models help understand tests and underlying constructs.

Secondary multilevel mixed-effects modelling of seroprevalence trends of Crimean-Congo haemorrhagic fever.

Background: Some review papers and meta-analyses have investigated seroprevalence and fatality trends of the Crimean-Congo hemorrhagic fever (CCHF), but it is not clear if its seroprevalence is increasing. Aim: To investigate the trend in the seroprevalence of CCHF. Methods: We conducted a secondary analysis of the results of a meta-analysis of the seroprevalence of CCHF published in 2019. We used a multilevel mixed effects Poisson regression to find the predictors of seropositivity. To explain the magnitude effect, we reported an incidence rate ratio (IRR) with a 95% confidence interval (CI). We conducted multilevel modeling using Stata 14 for data analysis. Results: In the fixed effects model, time was significantly associated with increased seropositivity (IRR = 1.025, 95% CI = 1.021-1.030), and no significant association was found for local sampling (IRR = 1.026, 95% CI = 0.988-1.065). In the mixed effects model, random intercepts of the country and parallel of latitude were applied as 3 levels of the model (prevalence rate of each study, nested within countries and latitude parallel). Accordingly, time was significantly associated with a reduction of seropositivity (IRR = 0.899, 95% CI = 0.891-0.907), and local sampling was significantly associated with increased seropositivity (IRR = 2.477, 95% CI = 2.316-2.649). Conclusion: Despite reporting increasing trends for seroprevalence of CCHF in previous reviews and the fixed effects model of the present study, the secondary mixed effects modeling showed a decreasing trend. The multilevel generalized model is recommended for such temporal and spatial designs in the future.

Quantifying the HIV reservoir with dilution assays and deep viral sequencing.

People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but "latent" HIV still persists in viral reservoirs. Eliminating these reservoirs is the goal of HIV cure research. The quantitative viral outgrowth assay (QVOA) is commonly used to estimate the reservoir size, that is, the infectious units per million (IUPM) of HIV-persistent resting CD4+ T cells. A new variation of the QVOA, the ultra deep sequencing assay of the outgrowth virus (UDSA), was recently developed that further quantifies the number of viral lineages within a subset of infected wells. Performing the UDSA on a subset of wells provides additional information that can improve IUPM estimation. This paper considers statistical inference about the IUPM from combined dilution assay (QVOA) and deep viral sequencing (UDSA) data, even when some deep sequencing data are missing. Methods are proposed to accommodate assays with wells sequenced at multiple dilution levels and with imperfect sensitivity and specificity, and a novel bias-corrected estimator is included for small samples. The proposed methods are evaluated in a simulation study, applied to data from the University of North Carolina HIV Cure Center, and implemented in the open-source R package SLDeepAssay.

A comparison of single- and double-threshold ROC plots for mixture distributions.

The receiver operating characteristics (ROC) analysis is commonly used in clinical settings to check the performance of a single threshold for distinguishing population-wise bimodal-distributed test results. However, for population-wise three-modal distributed test results, a single threshold ROC (stROC) analysis showed poor discriminative performance. The purpose of this study is to use a double-threshold ROC analysis for the three-modal distributed test results to provide better discriminative performance than the stROC analysis. A double-threshold receiver operating characteristic plot (dtROC) is constructed by replacing the single threshold with a double threshold. The sensitivity and specificity coordinates are chosen to maximize sensitivity for a given specificity value. Besides a simulation study assuming a mixture of lognormal, Poisson, and Weibull distributions, a clinical application is examined by a secondary data analysis of palpation test results of the C7 spinous process using the modified thorax-rib static technique. For the assumed mixture models, the discrimination performance of dtROC analysis outperforms the stROC analysis (area under ROC (AUROC) increased from 0.436 to 0.983 for lognormal distributed test results, 0.676 to 0.752 for the Poisson distribution, and 0.674 to 0.804 for Weibull distribution).

Linear Bayesian Estimation of Misrecorded Poisson Distribution.

Parameter estimation is an important component of statistical inference, and how to improve the accuracy of parameter estimation is a key issue in research. This paper proposes a linear Bayesian estimation for estimating parameters in a misrecorded Poisson distribution. The linear Bayesian estimation method not only adopts prior information but also avoids the cumbersome calculation of posterior expectations. On the premise of ensuring the accuracy and stability of computational results, we derived the explicit solution of the linear Bayesian estimation. Its superiority was verified through numerical simulations and illustrative examples.

eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.

Background: Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. Results: We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression. Conclusions: eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

[The Validity of the Poisson Distribution to Analyze Microbial Colony Counts on Agar Plates for Food Samples].

Microbial colony counts of food samples in microbiological examinations are one of the most important items. The probability distributions for the colony counts per agar plate at the dilution of counting had not been intensively studied so far. Recently we analyzed the colony counts of food samples with several probability distributions using the Pearson's chi-square value by the "traditional" statistics as the index of fit [Fujikawa and Tsubaki, Food Hyg.Saf.Sc., 60, 88-95 (2019)]. As a result, the selected probability distributions depended on the samples. In this study we newly selected a probability distribution, namely a statistical model, suitable for the above data with the method of maximum likelihood from the probabilistic point of view. The Akaike's Information Criterion (AIC) was used as the index of fit. Consequently, the Poisson model were better than the negative binomial model for all of four food samples. The Poisson model was also better than the binomial for three of four microbial culture samples. With Baysian Information Criterion (BIC), the Poisson model was also better than these two models for all the samples. These results suggested that the Poisson distribution would be the best model to estimate the colony counts of food samples. The present study would be the first report on the statistical model selection for the colony counts of food samples with AIC and BIC.

A Bayesian nonparametric approach to correct for underreporting in count data.

We propose a nonparametric compound Poisson model for underreported count data that introduces a latent clustering structure for the reporting probabilities. The latter are estimated with the model's parameters based on experts' opinion and exploiting a proxy for the reporting process. The proposed model is used to estimate the prevalence of chronic kidney disease in Apulia, Italy, based on a unique statistical database covering information on m = 258 municipalities obtained by integrating multisource register information. Accurate prevalence estimates are needed for monitoring, surveillance, and management purposes; yet, counts are deemed to be considerably underreported, especially in some areas of Apulia, one of the most deprived and heterogeneous regions in Italy. Our results agree with previous findings and highlight interesting geographical patterns of the disease. We compare our model to existing approaches in the literature using simulated as well as real data on early neonatal mortality risk in Brazil, described in previous research: the proposed approach proves to be accurate and particularly suitable when partial information about data quality is available.

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.

Hypothesis testing of Poisson rates in COVID-19 offspring distributions.

In the present study, we undertake the task of hypothesis testing in the context of Poisson-distributed data. The primary objective of our investigation is to ascertain whether two distinct sets of discrete data share the same Poisson rate. We delve into a comprehensive review and comparative analysis of various frequentist and Bayesian methodologies specifically designed to address this problem. Among these are the conditional test, the likelihood ratio test, and the Bayes factor. Additionally, we employ the posterior predictive p-value in our analysis, coupled with its corresponding calibration procedures. As the culmination of our investigation, we apply these diverse methodologies to test both simulated datasets and real-world data. The latter consists of the offspring distributions linked to COVID-19 cases in two disparate geographies - Hong Kong and Rwanda. This allows us to provide a practical demonstration of the methodologies' applications and their potential implications in the field of epidemiology.

An analytical approach to confidence interval estimation of river microplastic sampling.

Microplastics (MPs), plastic particles <5 mm in diameter, are emerging ubiquitous pollutants in natural environments, including freshwater ecosystems. As rivers facilitate efficient transport among landscapes, monitoring is crucial for elucidating the origin, dynamics, and fate of MPs. However, standardized methodologies for in situ sampling in freshwater environments remain undefined to date. Specifically, evaluating the sampling error of MP concentration estimates is crucial for comparing results among studies. This study proposes a novel method for computing confidence intervals (CIs) from a single estimate of numerical concentration (expressed in particles·m-3). MPs are expected to disperse according to purely random processes, such as turbulent diffusion, and to consequently exhibit a random distribution pattern that follows a Poisson point process. Accordingly, the present study introduced a framework based on the Poisson point process to compute CIs, which were validated using MP samples from two urban rivers in Chiba, Japan, obtained using a mesh with an opening size of 335 µm. Random number simulations revealed that the CIs were applicable when ≥10 MPs were present in a sample. Further, when ≥50 MPs were present in a sample, the sampling error (95% CI) was within ±30% of the concentration estimates. The proposed framework allows for the intercomparison of single river MP samples despite the lack of sample replicates. Further, the present study emphasizes that the volume of sampled river water is the only controllable parameter that can reduce the sampling error.

Tracking community infection dynamics of COVID-19 by monitoring SARS-CoV-2 RNA in wastewater, counting positive reactions by qPCR.

Wastewater-based epidemiology has proved useful for monitoring the COVID-19 infection dynamics in communities. However, in regions of low prevalence, low concentrations of SARS-CoV-2 RNA in wastewater make this difficult. Here, we used real-time reverse-transcription PCR (RT-qPCR) to monitor SARS-CoV-2 RNA in wastewater from October 2020 to December 2022 during the third, fourth, fifth, sixth, seventh, and eighth waves of the COVID-19 outbreak in Japan. Viral RNA was below the limit of detection in all samples during the third and fourth waves. However, by counting the number of positive replicates in qPCR of each sample, we found that the positive ratio to all replicates in wastewater was significantly correlated with the number of clinically confirmed cases by the date of symptom onset during the third, fourth, and fifth waves. Time-step analysis indicated that, for 2 days either side of symptom onset, COVID-19 patients excreted in their feces large amounts of virus that wastewater surveillance could detect. We also demonstrated that the viral genome copy number in wastewater, as estimated from the positive ratio of SARSA-CoV-2 RNA, was correlated with the number of clinically confirmed cases. The positive count method is thus useful for tracing COVID-19 dynamics in regions of low prevalence.

Spatial Distribution of Flagellated Microalgae Chlamydomonas reinhardtii in a Quasi-Two-Dimensional Space.

Although the phenomenon of collective order formation by cell-cell interactions in motile cells, microswimmers, has been a topic of interest, most studies have been conducted under conditions of high cell density, where the space occupancy of a cell population relative to the space size ϕ>0.1 (ϕ is the area fraction). We experimentally determined the spatial distribution (SD) of the flagellated unicellular green alga Chlamydomonas reinhardtii at a low cell density (ϕ≈0.01) in a quasi-two-dimensional (thickness equal to cell diameter) restricted space and used the variance-to-mean ratio to investigate the deviation from the random distribution of cells, that is, do cells tend to cluster together or avoid each other? The experimental SD is consistent with that obtained by Monte Carlo simulation, in which only the excluded volume effect (EV effect) due to the finite size of cells is taken into account, indicating that there is no interaction between cells other than the EV effect at a low cell density of ϕ≈0.01. A simple method for fabricating a quasi-two-dimensional space using shim rings was also proposed.