Parasite abundance distribution as a model of host-parasite relationships between monogeneans Gyrodactylus spp. and cage-reared rainbow trout Oncorhynchus mykiss.

Aggregation is a fundamental feature of parasite distribution in the host population, but the biological implications of the aggregation indices used to describe the relationships between the populations of parasites and hosts are not evident. It is speculated that the form of distribution in each case is predicated on the host's varying resistance to the infection, which is hard to control, making it difficult to adequately interpret the index values. This paper examines several cases from trout farms in Russian Karelia to explore the monogenean Gyrodactylus spp. infection in rainbow trout of varying ages. The genetic homogeneity of cage-reared fish and the direct life cycle of the helminths make the relationship between the species more lucid than in natural host-parasite systems. The results give no ground to speak of any specific patterns: as well as in the natural systems, the infection rates in trout vary widely, i.e., the helminth distribution has not become more uniform; the observed distributions in all cases are adequately approximated by the negative binomial model; the positive abundance-occupancy relationships (AORs) and abundance-variance relationships (AVRs) common for parasitic systems apply to the basic infection parameters. The form of the negative binomial distribution is shaped by two parameters-k and θ, the former being a metric of the infection variability, which depends on the host's individual resistance, and the latter representing the parasites' reproduction and establishment success rates. A rise in the parameter k indicates increased aggregation and a higher parameter θ points to a more uniform frequency distribution. These parameters can be used as a representative tool for monitoring the parasite communities in salmonid fishes, including in aquaculture.

Evaluating the impact of occupational noise exposure on workplace fatal and nonfatal injuries in the U.S. (2006-2020).

OBJECTIVES: This study assessed the relationship between occupational noise exposure and the incidence of workplace fatal injury (FI) and nonfatal injury (NFI) in the United States from 2006 to 2020. It also examined whether distinct occupational and industrial clusters based on noise exposure characteristics demonstrated varying risks for FI and NFI. METHODS: An ecological study design was utilized, employing data from the U.S. Bureau of Labor Statistics for FI and NFI and demographic data, the U.S. Census Bureau for occupation/industry classification code lists, and the U.S./Canada Occupational Noise Job Exposure Matrix for noise measurements. We examined four noise metrics as predictors of FI and NFI rates: mean Time-Weighted Average (TWA), maximum TWA, standard deviation of TWA, and percentage of work shifts exceeding 85 or 90 dBA for 619 occupation-years and 591 industry-years. K-means clustering was used to identify clusters of noise exposure characteristics. Mixed-effects negative binomial regression examined the relationship between the noise characteristics and FI/NFI rates separately for occupation and industry. RESULTS: Among occupations, we found significant associations between increased FI rates and higher mean TWA (IRR: 1.06, 95% CI: 1.01-1.12) and maximum TWA (IRR: 1.10, 95% CI: 1.07-1.14), as well as TWA exceedance (IRR: 1.04, 95% CI: 1.01-1.07). Increased rates of NFI were found to be significantly associated with maximum TWA (IRR: 1.06, 95% CI: 1.04-1.09) and TWA exceedance (IRR: 1.03, 95% CI: 1.01-1.05). In addition, occupations with both higher exposure variability (IRR with FI rate: 1.49, 95% CI: 1.23-1.80; IRR with NFI rate: 1.40, 95% CI: 1.14-1.73) and higher level of sustained exposure (IRR with FI rate: 1.27, 95% CI: 1.12-1.44; IRR with NFI rate: 1.21, 95% CI: 1.05-1.39) were associated with higher rates of FI and NFI compared to occupations with low noise exposure. Among industries, significant associations between increased NFI rates and higher mean TWA (IRR: 1.05, 95% CI: 1.02-1.08) and maximum TWA (IRR: 1.06, 95% CI: 1.04-1.08) were observed. Unlike the occupation-specific analysis, industries with higher exposure variability and higher sustained exposures did not display significantly higher FI/NFI rates compared to industries with low exposure. CONCLUSIONS: The results suggest that occupational noise exposure may be an independent risk factor for workplace FIs/NFIs, particularly for workplaces with highly variable noise exposures. The study highlights the importance of comprehensive occupational noise assessments.

Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model.

BACKGROUND: It is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index, an indicator of how large of a keyword is in Baidu's search volume, in the early warning and predicting the epidemic trend of COVID-19. METHODS: The daily number of cases and the Baidu Search Index of 8 keywords (weighted by population) from December 1, 2019 to March 15, 2020 were collected and analyzed with times series and Spearman correlation with different time lag. To predict the daily number of COVID-19 cases using the Baidu Search Index, Zero-inflated negative binomial regression was used in phase 1 and negative binomial regression model was used in phase 2 and phase 3 based on the characteristic of independent variable. RESULTS: The Baidu Search Index of all keywords in Wuhan was significantly higher than Hubei (excluded Wuhan) and China (excluded Hubei). Before the causative pathogen was identified, the search volume of "Influenza" and "Pneumonia" in Wuhan increased with the number of new onset cases, their correlation coefficient was 0.69 and 0.59, respectively. After the pathogen was public but before COVID-19 was classified as a notifiable disease, the search volume of "SARS", "Pneumonia", "Coronavirus" in all study areas increased with the number of new onset cases with the correlation coefficient was 0.69 ~ 0.89, while "Influenza" changed to negative correlated (rs: -0.56 ~ -0.64). After COVID-19 was closely monitored, the Baidu Search Index of "COVID-19", "Pneumonia", "Coronavirus", "SARS" and "Mask" could predict the epidemic trend with 15 days, 5 days and 6 days lead time, respectively in Wuhan, Hubei (excluded Wuhan) and China (excluded Hubei). The predicted number of cases would increase 1.84 and 4.81 folds, respectively than the actual number of cases in Wuhan and Hubei (excluded Wuhan) from 21 January to 9 February. CONCLUSION: The Baidu Search Index could be used in the early warning and predicting the epidemic trend of COVID-19, but the search keywords changed in different period. Considering the time lag from onset to diagnosis, especially in the areas with medical resources shortage, internet search data can be a highly effective supplement of the existing surveillance system.

Mathematical Modeling of Mating Probability and Fertile Egg Production in Helminth Parasites.

In this work, we obtained a general formulation for the mating probability and fertile egg production in helminth parasites, focusing on the reproductive behavior of polygamous parasites and its implications for transmission dynamics. By exploring various reproductive variables in parasites with density-dependent fecundity, such as helminth parasites, we departed from the traditional assumptions of Poisson and negative binomial distributions to adopt an arbitrary distribution model. Our analysis considered critical factors such as mating probability, fertile egg production, and the distribution of female and male parasites among hosts, whether they are distributed together or separately. We show that the distribution of parasites within hosts significantly influences transmission dynamics, with implications for parasite persistence and, therefore, with implications in parasite control. Using statistical models and empirical data from Monte Carlo simulations, we provide insights into the complex interplay of reproductive variables in helminth parasites, enhancing our understanding of parasite dynamics and the transmission of parasitic diseases.

A hybrid approach for modeling bicycle crash frequencies: Integrating random forest based SHAP model with random parameter negative binomial regression model.

To effectively capture and explain complex, nonlinear relationships within bicycle crash frequency data and account for unobserved heterogeneity simultaneously, this study proposes a new hybrid framework that combines the Random Forest-based SHapley Additive exPlanations (RF-SHAP) method with a random parameter negative binomial regression model (RPNB). First, four machine learning algorithms, including random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), and Extreme Gradient Boosting (XGBoost), were compared for variable importance calculation. The RF algorithm, demonstrating the best performance, was selected and integrated into an interpretable machine learning-based method (i.e., RF-SHAP) to provide an interpretable measure of each variable's impact, which is critical for understanding the model's predictions results. Finally, the RF-SHAP method was combined with the RPNB model to explore individual-specific variations that influence crash frequency predictions. Using 288 traffic analysis zones (TAZs) in Greater London and various regional risk factors for bicycle crash frequency, the proposed framework was validated. The results indicate that the proposed framework demonstrates improved prediction accuracy and better factor interpretation in analyzing bicycle crash frequency. The model exhibits consistent Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, indicating its reliable explanatory power. Furthermore, there is a significant improvement in the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This suggests that the proposed model effectively combines the explanatory power of statistical models with the forecasting powers of data-driven models. The interpretability of SHAP values, coupled with the causal insights from RPNB, provides policymakers with actionable information to develop targeted interventions.

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Objective: To construct a model for predicting recidivism in violence in community-based schizophrenia spectrum disorder patients (SSDP) by adopting a joint modeling method. Methods: Based on the basic data on severe mental illness in Southwest China between January 2017 and June 2018, 4565 community-based SSDP with baseline violent behaviors were selected as the research subjects. We used a growth mixture model (GMM) to identify patterns of medication adherence and social functioning. We then fitted the joint model using a zero-inflated negative binomial regression model and compared it with traditional static models. Finally, we used a 10-fold training-test cross validation framework to evaluate the models' fitting and predictive performance. Results: A total of 157 patients (3.44%) experienced recidivism in violence. Medication compliance and social functioning were fitted into four patterns. In the counting model, age, marital status, educational attainment, economic status, historical types of violence, and medication compliance patterns were predictive factors for the frequency of recidivism of violence (P<0.05). In the zero-inflated model, age, adverse drug reactions, historical types of violence, medication compliance patterns, and social functioning patterns were predictive factors for the recidivism in violence (P<0.05). For the joint model, the average value of Akaike information criterion (AIC) for the train set was 776.5±9.4, the average value of root mean squared error (RMSE) for the testing set was 0.168±0.013, and the average value of mean absolute error (MAE) for the testing set was 0.131±0.018, which were all lower than those of the traditional static models. Conclusion: Joint modeling is an effective statistical strategy for identifying and processing dynamic variables, exhibiting better predictive performance than that of the traditional static models. It can provide new ideas for promoting the construction of comprehensive intervention systems.

Exploring the relationship between blood platelet and other components utilizing count regression: A cross-sectional study in Bangladesh.

Background and Aims: Blood, vital for transporting nutrients and maintaining balance, comprises red blood cells, white blood cells, and platelets, each pivotal. Imbalances lead to issues-low red cells cause fatigue (anemia), high white cells hint at infection, low counts raise infection risks. Using trendy statistical approaches, investigating the complex link between platelet counts and numerous blood components. Our investigation, leveraging count regression approaches, revealed deep insights into the interaction between platelet counts and other important hematological markers. Methods: A cross-sectional study utilized data from 3120 individuals, including both male and female participants, who visited these hospitals between June 16, 2022 and December 17, 2022, to assess their blood samples through testing by using convenience non-parametric sampling framework. Platelet count was taken into account as a measure of outcome in this research. This specific study region was chosen for its easy accessibility, which helped the seamless execution of the data-gathering technique. Count regression, negative binomial regression, and quasi-Poisson regression techniques have been employed for examining relationship of the data sets. Results: Three different count regression models were utilized to assess the proper association between the response and the relevant covariates and we found negative binomial count regression model (Akaike information criterion = 76.55, Bayesian information criterion = 76.59, and deviance = 3.14) was providing comparatively better performance than others. Based on the chosen model we found white blood cell, erythrocyte sedimentation rate, and eosinophils are significant but neutrophil, monocyte, and lymphocyte are not significant. We have also gone through proper model adequacy checking for our selected model and we found enough evidence to justify our model. Conclusion: From the result, we found insightful remarks into the mechanisms involved in platelet production and regulation, which can aid in developing increased effective treatments and interventions to maintain optimal platelet levels and prevent health problems related to abnormal platelet counts.

Quantifying the heterogeneity impact of risk factors on regional bicycle crash frequency: A hybrid approach of clustering and random parameter model.

The existence of internal and external heterogeneity has been established by numerous studies across various fields, including transportation and safety analysis. The findings from these studies underscore the complexity of crash data and the multifaceted nature of risk factors involved in accidents. However, most studies consider the effects of unobserved heterogeneity from one perspective -- either within clusters (internal) or between clusters (external) -- and do not investigate the biases from both simultaneously on crash frequency analysis. To fill this gap, this study proposes a hybrid approach combining latent class cluster analysis with the random parameter negative binomial regression model (LCA-RPNB) to explore the association between risk factors and bicycle crash frequency. First, the bicycle crash data is categorized into three clusters using LCA based on crash features such as gender, trip purposes, weather, and light conditions. Then, the separated crash frequency models for different clusters and the overall model are developed based on RPNB using regional factors of crash locations as independent variables and the crash frequency of different clusters respectively as dependent variables. The hybrid approach enables a comprehensive examination of internal and external heterogeneities among bicycle crash frequency factors simultaneously. Results suggest that the proposed hybrid approach exhibits superior fitting and predictive performance compared to the model only considers the effects of unobserved heterogeneity from one perspective with the lower values of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This approach can help policymakers and urban planners to design more effective safety interventions by understanding the distinct needs of different bicyclist clusters and the specific factors that contribute to crash risk in each group.

Optimizing Corn Crop Protection: The First Sampling Plan for Controlling Dalbulus maidis (Hemiptera: Cicadellidae).

Corn (Zea mays) is the most widely planted crop in the world. Dalbulus maidis (Hemiptera: Cicadellidae) is currently a primary corn pest. The starting point for the development of pest control decision-making systems is the determination of a conventional sampling plan. Therefore, this study aimed to determine a practical conventional sampling plan for D. maidis in corn crops. Insect density was evaluated in 28 commercial fields. Subsequently, D. maidis densities were sampled from fields ranging from 1 to 100 ha. Insect density conformed to a negative binomial distribution in 89.29% of the fields. The insect densities determined using the sampling plan had a low error rate (up to 15%). Sampling time and costs ranged from 2.06 to 39.45 min/ha and 0.09 to 1.81 USD/ha for fields of 1-100 ha, respectively. These results provide the first precise and representative conventional sampling plan for scouting D. maidis adults grown in corn fields. Therefore, the conventional sampling plan for D. maidis determined in this study is practical and can be incorporated into integrated pest management programs for corn crops owing to its representativeness, precision, speed, and low cost.

The negative Binomial-Lindley model with Time-Dependent Parameters: Accounting for temporal variations and excess zero observations in crash data.

Crash counts are non-negative integer events often analyzed using crash frequency models such as the negative binomial (NB) distribution. However, due to their random and infrequent nature, crash data usually exhibit unique characteristics, such as excess zero observations that the NB distribution cannot adequately model. The negative binomial-Lindley (NBL) and random parameters negative binomial-Lindley (RPNBL) models have been proposed to address this limitation. Despite addressing the issues of excess zero observations, these models may not fully account for unobserved heterogeneity resulting from temporal variations in crash data. In addition, many variables, such as traffic volume, speed, and weather, change with time. Therefore, the analyst often requires disaggregated data to account for their variations. For example, it is recommended to use monthly crash datasets to better account for temporally varying weather variables compared to yearly crash data. Using temporally disaggregated data not only adds the complexity of the temporal variations issue in data but also compounds the issue of excess zero observations. To address these issues, this paper introduces a new variant of the NBL model with coefficients and Lindley parameters that vary by time. The derivations and characteristics of the model are discussed. Then, the model is illustrated using a simulation study. Subsequently, the model is applied to two empirical crash datasets collected on rural principal and minor arterial roads in Texas. These datasets include several time-dependent variables such as monthly traffic volume, standard deviation of speed, and precipitation and exhibit unique characteristics such as excess zero observations. The results of several goodness-of-fit (GOF) measures indicate that using the NBL model with time-dependent parameters enhances the model fit compared to the NB, NBL, and the NB model with time-dependent parameters. Findings derived from crash data collected from both rural minor and principal arterial roads in Texas suggest that the variables denoting the median presence and wider shoulder width are associated with a potential decrease in crash occurrences. Moreover, higher variations in speed and wider road surfaces are linked to a potential increase in crash frequency. Similarly, a higher monthly average daily traffic (Monthly ADT) positively correlates with crash frequency. We also found that it is important to account for temporal variations using time-dependent parameters.

An interpretable Bayesian clustering approach with feature selection for analyzing spatially resolved transcriptomics data.

Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many non-spatial and spatial clustering approaches have been used to partition the entire region into mutually exclusive spatial domains based on the SRT high-dimensional molecular profile, most require an ad hoc selection of less interpretable dimensional-reduction techniques. To overcome this challenge, we propose a zero-inflated negative binomial mixture model to cluster spots or cells based on their molecular profiles. To increase interpretability, we employ a feature selection mechanism to provide a low-dimensional summary of the SRT molecular profile in terms of discriminating genes that shed light on the clustering result. We further incorporate the SRT geospatial profile via a Markov random field prior. We demonstrate how this joint modeling strategy improves clustering accuracy, compared with alternative state-of-the-art approaches, through simulation studies and 3 real data applications.

Emotional and cognitive influences on alcohol consumption in middle-aged and elderly Tanzanians: a population-based study.

Alcohol consumption in Tanzania exceeds the global average. While sociodemographic difference in alcohol consumption in Tanzania have been studied, the relationship between psycho-cognitive phenomena and alcohol consumption has garnered little attention. Our study examines how depressive symptoms and cognitive performance affect alcohol consumption, considering sociodemographic variations. We interviewed 2299 Tanzanian adults, with an average age of 53 years, to assess their alcohol consumption, depressive symptoms, cognitive performance, and sociodemographic characteristics using a zero-inflated negative binomial regression model. The logistic portion of our model revealed that the likelihood alcohol consumption increased by 8.4% (95% confidence interval [CI] 3.6%, 13.1%, p < 0.001) as depressive symptom severity increased. Conversely, the count portion of the model indicated that with each one-unit increase in the severity of depressive symptoms, the estimated number of drinks decreased by 2.3% (95% CI [0.4%, 4.0%], p = .016). Additionally, the number of drinks consumed decreased by 4.7% (95% CI [1.2%, 8.1%], p = .010) for each increased cognitive score. Men exhibited higher alcohol consumption than women, and Christians tended to consume more than Muslims. These findings suggest that middle-aged and elderly adults in Tanzania tend to consume alcohol when they feel depressed but moderate their drinking habits by leveraging their cognitive abilities.

Excess multi-cause mortality linked to influenza virus infection in China, 2012-2021: a population-based study.

Objectives: The aim of this study is to estimate the excess mortality burden of influenza virus infection in China from 2012 to 2021, with a concurrent analysis of its associated disease manifestations. Methods: Laboratory surveillance data on influenza, relevant population demographics, and mortality records, including cause of death data in China, spanning the years 2012 to 2021, were incorporated into a comprehensive analysis. A negative binomial regression model was utilized to calculate the excess mortality rate associated with influenza, taking into consideration factors such as year, subtype, and cause of death. Results: There was no evidence to indicate a correlation between malignant neoplasms and any subtype of influenza, despite the examination of the effect of influenza on the mortality burden of eight diseases. A total of 327,520 samples testing positive for influenza virus were isolated between 2012 and 2021, with a significant decrease in the positivity rate observed during the periods of 2012-2013 and 2019-2020. China experienced an average annual influenza-associated excess deaths of 201721.78 and an average annual excess mortality rate of 14.53 per 100,000 people during the research period. Among the causes of mortality that were examined, respiratory and circulatory diseases (R&C) accounted for the most significant proportion (58.50%). Fatalities attributed to respiratory and circulatory diseases exhibited discernible temporal patterns, whereas deaths attributable to other causes were dispersed over the course of the year. Conclusion: Theoretically, the contribution of these disease types to excess influenza-related fatalities can serve as a foundation for early warning and targeted influenza surveillance. Additionally, it is possible to assess the costs of prevention and control measures and the public health repercussions of epidemics with greater precision.

A Statistical Analysis of the Impact of Gun Ownership on Mass Shootings in the USA Between 2013 and 2022.

Mass shootings (incidents with four or more people shot in a single event, not including the shooter) are becoming more frequent in the United States, posing a significant threat to public health and safety in the country. In the current study, we intended to analyze the impact of state-level prevalence of gun ownership on mass shootings-both the frequency and severity of these events. We applied the negative binomial generalized linear mixed model to investigate the association between gun ownership rate, as measured by a proxy (i.e., the proportion of suicides committed with firearms to total suicides), and population-adjusted rates of mass shooting incidents and fatalities at the state level from 2013 to 2022. Gun ownership was found to be significantly associated with the rate of mass shooting fatalities. Specifically, our model indicated that for every 1-SD increase-that is, for every 12.5% increase-in gun ownership, the rate of mass shooting fatalities increased by 34% (p value < 0.001). However, no significant association was found between gun ownership and rate of mass shooting incidents. These findings suggest that restricting gun ownership (and therefore reducing availability to guns) may not decrease the number of mass shooting events, but it may save lives when these events occur.

Good agricultural practices (GAP) adoption intensity and production constraints in apple orchards of western Nepal.

The declaration of 'Fruits Decade 2016/17-2026/27' and the enaction of the 'NepalGAP Scheme' by the Government of Nepal has redirected increased public investments to promote apple production and marketability in the western high hills of Nepal. This study explores major good agricultural practices (GAP) related to orchard management, factors influencing their adoption intensity, and key underlying constraints to production using cross-sectional survey data from apple growers in Dolpa district, Nepal. The results showed that farmers mostly adopted GAP such as frequent weeding, intercropping, and nutrient management in apple orchards. Based on the negative binomial regression estimates, household characteristics such as gender of the orchard owner, experience, and number of literate household members were found influential in determining the GAP adoption intensity. The analysis of the problem severity index implied that apple production is mostly constrained by limited access to production inputs and transportation. The findings provide useful insights to the farmers and policymakers regarding the current scenario of GAP adoption along with the diversity of barriers that severely limits the realization of apple production potential in western Nepal.

Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved transcriptomics data.

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.

The development of a road safety policy index and its application in evaluating the effects of road safety policy.

The paper presents an exploratory study of a road safety policy index developed for Norway. The index consists of ten road safety measures for which data on their use from 1980 to 2021 are available. The ten measures were combined into an index which had an initial value of 50 in 1980 and increased to a value of 185 in 2021. To assess the application of the index in evaluating the effects of road safety policy, negative binomial regression models and multivariate time series models were developed for traffic fatalities, fatalities and serious injuries, and all injuries. The coefficient for the policy index was negative, indicating the road safety policy has contributed to reducing the number of fatalities and injuries. The size of this contribution can be estimated by means of at least three estimators that do not always produce identical values. There is little doubt about the sign of the relationship: a stronger road safety policy (as indicated by index values) is associated with a larger decline in fatalities and injuries. A precise quantification is, however, not possible. Different estimators of effect, all of which can be regarded as plausible, yield different results.

A nested grouped random parameter negative binomial model for modeling segment-level crash counts.

In this study, a nested grouped random parameter negative binomial framework is proposed to model crash counts at the segment level, a three-level longitudinal framework. The proposed model accounts for correlations along county routes and over time and thus includes a time variable, the year index, to analyze crash counts. The model is applied to crashes on undivided two-lane arterial roads in Ohio from 2012 to 2017. The results present two variants of the model: one with varying intercepts and fixed slopes and the other with varying intercepts and slopes. Both variants have comparable interpretations concerning the fixed parameters, but the latter variant exhibits a significantly improved fit and provides additional information on the interpretations. The results show a significant quadratic relationship between the time variable and the crash count, indicating that, on average, the crash count of segments increases with a decreasing rate as time variable increases. Regarding random parameters, the findings show that 17% of segments within routes and 2% of routes exhibit crash counts that decrease at accelerating downward trend as time variable increases. The effect of the natural logarithm of the segment length varies significantly across different routes, with an increase in this value primarily leading to an increase in crashes. On the other hand, the effect of the total shoulder width also varies across routes, but unlike the former, an increase in this value generally results in a decrease in crashes. The proposed model shows high forecast accuracy for crash count prediction, making it a valuable tool for informed decision-making in safety improvement.

Smoothing regression and impact measures for accidents of traffic flows.

Traffic pattern identification and accident evaluation are essential for improving traffic planning, road safety, and traffic management. In this paper, we establish classification and regression models to characterize the relationship between traffic flows and different time points and identify different patterns of traffic flows by a negative binomial model with smoothing splines. It provides mean response curves and Bayesian credible bands for traffic flows, a single index, and the log-likelihood difference, for traffic flow pattern recognition. We further propose an impact measure for evaluating the influence of accidents on traffic flows based on the fitted negative binomial model. The proposed method has been successfully applied to real-world traffic flows, and it can be used for improving traffic management.

m6Aexpress-enet: Predicting the regulatory expression m6A sites by an enet-regularization negative binomial regression model.

As the most abundant mRNA modification, m6A controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific m6A sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple m6A sites in each gene could influence the prediction performance. To address the above challenges, we propose an elastic-net regularized negative binomial regression model (called m6Aexpress-enet) to predict which m6A site could potentially regulate its gene expression. Comprehensive evaluations on simulated datasets demonstrate that m6Aexpress-enet could achieve the top prediction performance. Applying m6Aexpress-enet on real MeRIP-seq data from human lymphoblastoid cell lines, we have uncovered the complex regulatory pattern of predicted m6A sites and their unique enrichment pathway of the constructed co-methylation modules. m6Aexpress-enet proves itself as a powerful tool to enable biologists to discover the mechanism of m6A regulatory gene expression. Furthermore, the source code and the step-by-step implementation of m6Aexpress-enet is freely accessed at https://github.com/tengzhangs/m6Aexpress-enet.