PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs.

Charting microRNA (miRNA) regulation across pathways is key to characterizing their function. Yet, no method currently exists that can quantify how miRNAs regulate multiple interconnected pathways or prioritize them for their ability to regulate coordinate transcriptional programs. Existing methods primarily infer one-to-one relationships between miRNAs and pathways using differentially expressed genes. We introduce PanomiR, an in silico framework for studying the interplay of miRNAs and disease functions. PanomiR integrates gene expression, mRNA-miRNA interactions and known biological pathways to reveal coordinated multi-pathway targeting by miRNAs. PanomiR utilizes pathway-activity profiling approaches, a pathway co-expression network and network clustering algorithms to prioritize miRNAs that target broad-scale transcriptional disease phenotypes. It directly resolves differential regulation of pathways, irrespective of their differential gene expression, and captures co-activity to establish functional pathway groupings and the miRNAs that may regulate them. PanomiR uses a systems biology approach to provide broad but precise insights into miRNA-regulated functional programs. It is available at https://bioconductor.org/packages/PanomiR.

Answering open questions in biology using spatial genomics and structured methods.

Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shapes, relative locations, movement, and interactions of cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present a framework to respond to this new avenue of research: four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each open question that may yield specific insights, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological understanding.

Applying a Bayesian Spatiotemporal Model to Examine Excess County-Level Cardiovascular Disease Death Rates During the COVID-19 Pandemic.

Amid the COVID-19 pandemic, national cardiovascular disease (CVD) death rates increased, especially among younger adults. County-level variation has not been documented. Using county-level CVD deaths (ICD-10 codes: I00-I99) from the US National Vital Statistics System, we developed a Bayesian multivariate spatiotemporal model to estimate excess CVD death rates in 2020 based on trends from 2010-2019 for adults aged 35-64 and ≥65 years. Among adults aged 35-64 years, 64.7% of counties experienced significant excess CVD death rates. The median county-level CVD death rate in 2020 was 150 per 100,000 persons, which exceeded the predicted rate for 2020 (median excess death rate: 11 per 100,000; median excess rate ratio: 1.08). Among adults aged ≥65 years, 15.2% of counties experienced significant excess CVD death rates. The median county-level CVD death rate was 1,546 per 100,000 in 2020, which exceeded the predicted rate in 2020 (median excess death rate: 48 per 100,000, median excess rate ratio: 1.03). Counties with significant excess death rates in 2020 were geographically dispersed. In 2020, disruptions of county-level CVD death rates were widespread, especially among younger adults, suggesting the continued importance of CVD prevention and treatment in younger adults in communities across the country.

Multistep estimators of the between-study covariance matrix under the multivariate random-effects model for meta-analysis.

A wide variety of methods are available to estimate the between-study variance under the univariate random-effects model for meta-analysis. Some, but not all, of these estimators have been extended so that they can be used in the multivariate setting. We begin by extending the univariate generalised method of moments, which immediately provides a wider class of multivariate methods than was previously available. However, our main proposal is to use this new type of estimator to derive multivariate multistep estimators of the between-study covariance matrix. We then use the connection between the univariate multistep and Paule-Mandel estimators to motivate taking the limit, where the number of steps tends toward infinity. We illustrate our methodology using two contrasting examples and investigate its properties in a simulation study. We conclude that the proposed methodology is a fully viable alternative to existing estimation methods, is well suited to sensitivity analyses that explore the use of alternative estimators, and should be used instead of the existing DerSimonian and Laird-type moments based estimator in application areas where data are expected to be heterogeneous. However, multistep estimators do not seem to outperform the existing estimators when the data are more homogeneous. Advantages of the new multivariate multistep estimator include its semi-parametric nature and that it is computationally feasible in high dimensions. Our proposed estimation methods are also applicable for multivariate random-effects meta-regression, where study-level covariates are included in the model.

Determining the optimum high-flow nasal cannula flow rate to achieve the desired fraction of inspired oxygen: A bench study.

BACKGROUND: Purpose: High-flow nasal cannula (HFNC) has many benefits in various clinical conditions. The original hypothesis suggests that the high and constant fraction of inspired oxygen (FiO2) is one of the main physiological effects. However, increasing evidence shows that there is a gap between the actual FiO2 and administered FiO2. We aimed to determine the actual FiO2 under different respiratory conditions and develop a regression model using a spontaneous breathing lung model. METHODS: A spontaneous breathing simulation model was built using an airway manikin and a model lung. The FiO2 was measured under different respiratory conditions with varying tidal volumes and respiratory and HFNC flow rates. The relationships between the respiratory parameters and actual FiO2 were determined and used to build the predictive model. RESULTS: The actual FiO2 was negatively correlated with respiratory rate and tidal volume and positively correlated with HFNC flow. The regression model could not be developed using simple respiratory parameters. Therefore, we introduced a new variable, defined as flow ratio, which equaled the HFNC flow divided by inspiratory flow. Our equation demonstrated that the actual FiO2 was mainly determined by the flow ratio in a non-linear relationship. Accordingly, a flow ratio greater than 1 did not ensure a constant high FiO2, whereas a flow ratio >1.435 could produce FiO2 >0.9. CONCLUSION: The FiO2 during HFNC was not constant even at sufficiently high oxygen flow compared with inspiratory flow. The predictive model showed that the actual FiO2 was mainly determined by the flow ratio.

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.

Time-Varying Exposures and Miscarriage: A Comparison of Statistical Models Through Simulation.

Epidemiologists face a unique challenge in measuring risk relationships involving time-varying exposures in early pregnancy. Each week in early pregnancy is distinct in its contribution to fetal development, and this period is commonly characterized by shifts in maternal behavior and, consequently, exposures. In this simulation study, we used alcohol as an example of an exposure that often changes during early pregnancy and miscarriage as an outcome affected by early exposures. Data on alcohol consumption patterns from more than 5,000 women in the Right From the Start cohort study (United States, 2000-2012) informed measures of the prevalence of alcohol exposure, the distribution of gestational age at cessation of alcohol use, and the likelihood of miscarriage by week of gestation. We then compared the bias and precision of effect estimates and statistical power from 5 different modeling approaches in distinct simulated relationships. We demonstrate how the accuracy and precision of effect estimates depended on alignment between model assumptions and the underlying simulated relationship. Approaches that incorporated data about patterns of exposure were more powerful and less biased than simpler models when risk depended on timing or duration of exposure. To uncover risk relationships in early pregnancy, it is critical to carefully define the role of exposure timing in the underlying causal hypothesis.

Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation.

BACKGROUND: The predictive capability of time-lapse monitoring (TLM) selection algorithms is influenced by patient characteristics, type and quality of data included in the analysis and the used statistical methods. Previous studies excluded DET cycles of which only one embryo implanted, introducing bias into the data. Therefore, we wanted to develop a TLM prediction model that is able to predict pregnancy chances after both single- and double embryo transfer (SET and DET). METHODS: This is a retrospective study of couples (n = 1770) undergoing an in vitro fertilization cycle at the Erasmus MC, University Medical Centre Rotterdam (clinic A) or the Reinier de Graaf Hospital (clinic B). This resulted in 2058 transferred embryos with time-lapse and pregnancy outcome information. For each dataset a prediction model was established by using the Embryo-Uterus statistical model with the number of gestational sacs as the outcome variable. This process was followed by cross-validation. RESULTS: Prediction model A (based on data of clinic A) included female age, t3-t2 and t5-t4, and model B (clinic B) included female age, t2, t3-t2 and t5-t4. Internal validation showed overfitting of model A (calibration slope 0.765 and area under the curve (AUC) 0.60), and minor overfitting of model B (slope 0.915 and AUC 0.65). External validation showed that model A was capable of predicting pregnancy in the dataset of clinic B with an AUC of 0.65 (95% CI: 0.61-0.69; slope 1.223, 95% CI: 0.903-1.561). Model B was less accurate in predicting pregnancy in the dataset of clinic A (AUC 0.60, 95% CI: 0.56-0.65; slope 0.671, 95% CI: 0.422-0.939). CONCLUSION: Our study demonstrates a novel approach to the development of a TLM prediction model by applying the EU statistical model. With further development and validation in clinical practice, our prediction model approach can aid in embryo selection and decision making for SET or DET.

A novel system for rapid conversion of Gardner embryo grades to linear scale numeric variables.

RESEARCH QUESTION: Can Gardner embryo grades be converted to numeric interval variables to improve the incorporation of embryo grading in statistical analyses? DESIGN: An equation that can be used to convert Gardner embryo grades to regular interval scale variables was developed: the numerical embryo quality scoring index (NEQsi). The NEQsi system was then validated with a retrospective chart analysis assessing IVF cycles (n = 1711) conducted at a single Canadian fertility centre between 2014 and 2022. Gardner embryo grades on file were assigned using EmbryoScope and converted to NEQsi scores. Descriptive statistics, univariate logistic regressions and generalized estimating equations with cycle outcomes were prepared to demonstrate the relationship between NEQsi score and probability of pregnancy. RESULTS: NEQsi produces interval numerical scores that range from 2 to 11. Patient case files in which single embryo transfers occurred (n = 1711) were examined and the Gardner embryo grades on file were converted to NEQsi scores. NEQsi scores ranged from 3 to 11, with a median score of 9. A positive linear relationship existed between the NEQsi scores and the probability of pregnancy (as assessed by quantitative ß-HCG). The NEQsi score was a significant predictor of pregnancy (P < 0.001). CONCLUSION: Gardner embryo grades can be converted to interval variables and used directly statistical analyses.

Sex estimation from long bones: a machine learning approach.

Sex estimation from skeletal remains is one of the crucial issues in forensic anthropology. Long bones can be a valid alternative to skeletal remains for sex estimation when more dimorphic bones are absent or degraded, preventing any estimation from the first intention methods. The purpose of this study was to generate and compare classification models for sex estimation based on combined measurement of long bones using machine learning classifiers. Eighteen measurements from four long bones (radius, humerus, femur, and tibia) were taken from a total of 2141 individuals. Five machine learning methods were employed to predict the sex: a linear discriminant analysis (LDA), penalized logistic regression (PLR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The different classification algorithms using all bones generated highly accuracy models with cross-validation, ranging from 90 to 92% on the validation sample. The classification with isolated bones ranked between 83.3 and 90.3% on the validation sample. In both cases, random forest stands out with the highest accuracy and seems to be the best model for our investigation. This study upholds the value of combined long bones for sex estimation and provides models that can be applied with high accuracy to different populations.

Sex assessment using the radius bone in a French sample when applying various statistical models.

Sex estimation of skeletal remains is one of the most important tasks in forensic anthropology. The radius bone is useful to develop standard guidelines for sex estimation across various populations and is an alternative when coxal or femoral bones are not available.The aim of the present study was to assess the sexual dimorphism from radius measurements in a French sample and compare the predictive accuracy of several modelling techniques, using both classical statistical methods and machine learning algorithms.A total of 78 left radii (36 males and 42 females) were used in this study. Sixteen measurements were made. The modelling techniques included a linear discriminant analysis (LDA), flexible discriminant analysis (FDA), regularised discriminant analysis (RDA), penalised logistic regression (PLR), random forests (RF) and support vector machines (SVM).The different statistical models showed an accuracy of classification that is greater than 94%. After selection of variables, the accuracies increased to 97%. The measurements made at the proximal part of the radius (sagittal and transversal diameters of the head, and sagittal diameter of the neck), at distal part (maximum width of the distal epiphysis) and of the entire bone (maximum length) stand out among the various models.The present study suggests that the radius bone constitutes a valid alternative for sex estimation of skeletal remains with comparable classification accuracies to the pelvis or femur and that the non-classical statistical models may provide a novel approach to sex estimation from the radius bone. However, the extrapolation of the current results cannot be made without caution because our sample was composed of very aged individuals.

Analysing cluster randomised controlled trials using GLMM, GEE1, GEE2, and QIF: results from four case studies.

BACKGROUND: Using four case studies, we aim to provide practical guidance and recommendations for the analysis of cluster randomised controlled trials. METHODS: Four modelling approaches (Generalized Linear Mixed Models with parameters estimated by maximum likelihood/restricted maximum likelihood; Generalized Linear Models with parameters estimated by Generalized Estimating Equations (1st order or second order) and Quadratic Inference Function, for analysing correlated individual participant level outcomes in cluster randomised controlled trials were identified after we reviewed the literature. We systematically searched the online bibliography databases of MEDLINE, EMBASE, PsycINFO (via OVID), CINAHL (via EBSCO), and SCOPUS. We identified the above-mentioned four statistical analytical approaches and applied them to four case studies of cluster randomised controlled trials with the number of clusters ranging from 10 to 100, and individual participants ranging from 748 to 9,207. Results were obtained for both continuous and binary outcomes using R and SAS statistical packages. RESULTS: The intracluster correlation coefficient (ICC) estimates for the case studies were less than 0.05 and are consistent with the observed ICC values commonly reported in primary care and community-based cluster randomised controlled trials. In most cases, the four methods produced similar results. However, in a few analyses, quadratic inference function produced different results compared to the generalized linear mixed model, first-order generalized estimating equations, and second-order generalized estimating equations, especially in trials with small to moderate numbers of clusters. CONCLUSION: This paper demonstrates the analysis of cluster randomised controlled trials with four modelling approaches. The results obtained were similar in most cases, however, for trials with few clusters we do recommend that the quadratic inference function should be used with caution, and where possible a small sample correction should be used. The generalisability of our results is limited to studies with similar features to our case studies, for example, studies with a similar-sized ICC. It is important to conduct simulation studies to comprehensively evaluate the performance of the four modelling approaches.

Use of days alive without life support and similar count outcomes in randomised clinical trials - an overview and comparison of methodological choices and analysis methods.

BACKGROUND: Days alive without life support (DAWOLS) and similar outcomes that seek to summarise mortality and non-mortality experiences are increasingly used in critical care research. The use of these outcomes is challenged by different definitions and non-normal outcome distributions that complicate statistical analysis decisions. METHODS: We scrutinized the central methodological considerations when using DAWOLS and similar outcomes and provide a description and overview of the pros and cons of various statistical methods for analysis supplemented with a comparison of these methods using data from the COVID STEROID 2 randomised clinical trial. We focused on readily available regression models of increasing complexity (linear, hurdle-negative binomial, zero-one-inflated beta, and cumulative logistic regression models) that allow comparison of multiple treatment arms, adjustment for covariates and interaction terms to assess treatment effect heterogeneity. RESULTS: In general, the simpler models adequately estimated group means despite not fitting the data well enough to mimic the input data. The more complex models better fitted and thus better replicated the input data, although this came with increased complexity and uncertainty of estimates. While the more complex models can model separate components of the outcome distributions (i.e., the probability of having zero DAWOLS), this complexity means that the specification of interpretable priors in a Bayesian setting is difficult. Finally, we present multiple examples of how these outcomes may be visualised to aid assessment and interpretation. CONCLUSIONS: This summary of central methodological considerations when using, defining, and analysing DAWOLS and similar outcomes may help researchers choose the definition and analysis method that best fits their planned studies. TRIAL REGISTRATION: COVID STEROID 2 trial, ClinicalTrials.gov: NCT04509973, ctri.nic.in: CTRI/2020/10/028731.

Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.).

Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01423-y.

Improving population-level refractive error monitoring via mixture distributions.

INTRODUCTION: Sampling and describing the distribution of refractive error in populations is critical to understanding eye care needs, refractive differences between groups and factors affecting refractive development. We investigated the ability of mixture models to describe refractive error distributions. METHODS: We used key informants to identify raw refractive error datasets and a systematic search strategy to identify published binned datasets of community-representative refractive error. Mixture models combine various component distributions via weighting to describe an observed distribution. We modelled raw refractive error data with a single-Gaussian (normal) distribution, mixtures of two to six Gaussian distributions and an additive model of an exponential and Gaussian (ex-Gaussian) distribution. We tested the relative fitting accuracy of each method via Bayesian Information Criterion (BIC) and then compared the ability of selected models to predict the observed prevalence of refractive error across a range of cut-points for both the raw and binned refractive data. RESULTS: We obtained large raw refractive error datasets from the United States and Korea. The ability of our models to fit the data improved significantly from a single-Gaussian to a two-Gaussian-component additive model and then remained stable with ≥3-Gaussian-component mixture models. Means and standard deviations for BIC relative to 1 for the single-Gaussian model, where lower is better, were 0.89 ± 0.05, 0.88 ± 0.06, 0.89 ± 0.06, 0.89 ± 0.06 and 0.90 ± 0.06 for two-, three-, four-, five- and six-Gaussian-component models, respectively, tested across US and Korean raw data grouped by age decade. Means and standard deviations for the difference between observed and model-based estimates of refractive error prevalence across a range of cut-points for the raw data were -3.0% ± 6.3, 0.5% ± 1.9, 0.6% ± 1.5 and -1.8% ± 4.0 for one-, two- and three-Gaussian-component and ex-Gaussian models, respectively. CONCLUSIONS: Mixture models appear able to describe the population distribution of refractive error accurately, offering significant advantages over commonly quoted simple summary statistics such as mean, standard deviation and prevalence.

Estimates of excess mortality during the COVID-19 pandemic strongly depend on subjective methodological choices.

Excess mortality is often used to assess the health impact of the COVID-19 pandemic. It involves comparing the number of deaths observed during the pandemic with the number of deaths that would counterfactually have been expected in the absence of the pandemic. However, published data on excess mortality often vary even for the same country. The reason for these discrepancies is that the estimation of excess mortality involves a number of subjective methodological choices. The aim of this paper was to summarize these subjective choices. In several publications, excess mortality was overestimated because population aging was not adjusted for. Another important reason for different estimates of excess mortality is the choice of different pre-pandemic reference periods that are used to estimate the expected number of deaths (e.g., only 2019 or 2015-2019). Other reasons for divergent results include different choices of index periods (e.g., 2020 or 2020-2021), different modeling to determine expected mortality rates (e.g., averaging mortality rates from previous years or using linear trends), the issue of accounting for irregular risk factors such as heat waves and seasonal influenza, and differences in the quality of the data used. We suggest that future studies present the results not only for a single set of analytic choices, but also for sets with different analytic choices, so that the dependence of the results on these choices becomes explicit.

Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region.

BACKGROUND: Cardiovascular diseases (CVD) are the predominant cause of early death worldwide. Identification of people with a high risk of being affected by CVD is consequential in CVD prevention. This study adopts Machine Learning (ML) and statistical techniques to develop classification models for predicting the future occurrence of CVD events in a large sample of Iranians. METHODS: We used multiple prediction models and ML techniques with different abilities to analyze the large dataset of 5432 healthy people at the beginning of entrance into the Isfahan Cohort Study (ICS) (1990-2017). Bayesian additive regression trees enhanced with "missingness incorporated in attributes" (BARTm) was run on the dataset with 515 variables (336 variables without and the remaining with up to 90% missing values). In the other used classification algorithms, variables with more than 10% missing values were excluded, and MissForest imputes the missing values of the remaining 49 variables. We used Recursive Feature Elimination (RFE) to select the most contributing variables. Random oversampling technique, recommended cut-point by precision-recall curve, and relevant evaluation metrics were used for handling unbalancing in the binary response variable. RESULTS: This study revealed that age, systolic blood pressure, fasting blood sugar, two-hour postprandial glucose, diabetes mellitus, history of heart disease, history of high blood pressure, and history of diabetes are the most contributing factors for predicting CVD incidence in the future. The main differences between the results of classification algorithms are due to the trade-off between sensitivity and specificity. Quadratic Discriminant Analysis (QDA) algorithm presents the highest accuracy (75.50 ± 0.08) but the minimum sensitivity (49.84 ± 0.25); In contrast, decision trees provide the lowest accuracy (51.95 ± 0.69) but the top sensitivity (82.52 ± 1.22). BARTm.90% resulted in 69.48 ± 0.28 accuracy and 54.00 ± 1.66 sensitivity without any preprocessing step. CONCLUSIONS: This study confirmed that building a prediction model for CVD in each region is valuable for screening and primary prevention strategies in that specific region. Also, results showed that using conventional statistical models alongside ML algorithms makes it possible to take advantage of both techniques. Generally, QDA can accurately predict the future occurrence of CVD events with a fast (inference speed) and stable (confidence values) procedure. The combined ML and statistical algorithm of BARTm provide a flexible approach without any need for technical knowledge about assumptions and preprocessing steps of the prediction procedure.

On Power-Efficient Low-Complexity Adaptation for D2D Resource Allocation with Interference Cancelation.

This paper presents a detailed framework for adaptive low-complexity and power-efficient resource allocation in decentralized device-to-device (D2D) networks. The adopted system model considers that active devices can directly communicate via specified signaling channels. Each D2D receiver attempts to allocate its D2D resources by selecting a D2D transmitter and one of its spectral channels that can meet its performance target. The process is performed adaptively over successive packet durations with the objective of limiting the transmit power on D2D links while reducing the processing complexity. The proposed D2D link adaptation scheme is modeled and analyzed under generalized channel conditions. It considers the random impact of potential D2D transmitters as well as the random number of co-channel interference sources on each D2D link. Interference cancelation schemes are also addressed to alleviate co-channel interference, which can ease the D2D resource allocation process. Generalized formulations for the statistics of the resulting signal-to-interference plus noise ratio (SINR) of the proposed adaptation scheme are presented. Moreover, generic analytical results were developed for some important performance measures as well as processing load measures. They facilitate tradeoff studies between the achieved performance and the processing complexity of the proposed scheme. Insightful results for the distributions of SINRs on individual D2D links under specific fading models are shown in this paper. The results herein add enhancements to some previous contributions and can handle various practical constraints.

Indoor exposure to polycyclic aromatic hydrocarbons associated with solid fuel use in rural China.

Polycyclic aromatic hydrocarbons (PAHs) are widespread environmental contaminants associated with various health risks including lung cancer. Indoor exposure to PAHs, particularly from the indoor burning of fuels, is significant; however, long-term large-scale assessments of indoor PAHs are hampered by high costs and time-consuming in field sampling and laboratory experiments. A simple fuel-based approach and statistical regression models were developed as a trial to predict indoor BaP, as a typical PAH, in China, and consequently spatiotemporal variations in indoor BaP and indoor exposure contributions were discussed. The results show that the national population-weighted indoor BaP concentration has decreased substantially from 46.1 ng/m3 in 1992 to 6.60 ng/m3 in 2017, primarily due to the increased use of clean energies for cooking and heating. Indoor BaP exposure contributed to > 70% of the total inhalation exposure in most cities, particularly in regions where solid fuels are widely utilized. With limited experimental observation data in building statistical models, quantitative results of the study are associated with high uncertainties; however, the study undoubtedly supports effective countermeasures on indoor PAHs from solid fuel use and the importance of promoting clean household energy usage to improve household air quality.

[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.